DATA ENGINEER PROFESSIONAL DATABRICKS

An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code: df = spark.read.format("parquet").load(f"/mnt/source/(date)") Which code block should be used to create the date Python variable used in the above code block?. date = spark.conf.get("date"). input_dict = input() date= input_dict["date"]. import sys date = sys.argv[1]. date = dbutils.notebooks.getParam("date"). dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date").

The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity. Each user should be able to execute workloads against their assigned clusters at any time of the day. Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster. "Can Manage" privileges on the required cluster. Workspace Admin privileges, cluster creation allowed, "Can Attach To" privileges on the required cluster. Cluster creation allowed, "Can Attach To" privileges on the required cluster. "Can Restart" privileges on the required cluster. Cluster creation allowed, "Can Restart" privileges on the required cluster.

When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?. Cluster: New Job Cluster; Retries: Unlimited; Maximum Concurrent Runs: Unlimited. Cluster: New Job Cluster; Retries: None; Maximum Concurrent Runs: 1. Cluster: Existing All-Purpose Cluster; Retries: Unlimited; Maximum Concurrent Runs: 1. Cluster: New Job Cluster; Retries: Unlimited; Maximum Concurrent Runs: 1. Cluster: Existing All-Purpose Cluster; Retries: None; Maximum Concurrent Runs: 1.

The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings. The below query is used to create the alert: SELECT MEAN(temperature), MAX(temperature), MIN(temperature) FROM recent_sensor_recordings GROUP BY sensor_id The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute. If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query. The recent_sensor_recordings table was unresponsive for three consecutive runs of the query. The source query failed to update properly for three consecutive minutes and then restarted. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query.

A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown. Which approach will allow this developer to review the current logic for this notebook?. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch. Merge all changes back to the main branch in the remote Git repository and clone the repo again. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository.

The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database. After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged). password = dbutils.secrets.get(scope='db_creds', kery='jdbc_password') print(passsword) df = (spark .read .format('jdbc') .option('url', connection) .option('dbtable', tablename) .option('user', username) .option('password', password) ) Which statement describes what will happen when the above code is executed?. The connection to the external table will fail; the string "REDACTED" will be printed. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text. The connection to the external table will succeed; the string value of password will be printed in plain text. The connection to the external table will succeed; the string "REDACTED" will be printed.

The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE". from pyspark.sql.functions import current_date model = mflow.pyfunc.spark_udf(spark, model_uri = 'models:/churn/prod') df = spark.table('customers') columns = ['account_age', 'time_since_last_seen', 'app_rating'] preds = (df.select('customer_id', model('columns').alias('predictions') current_date().alias('date')) The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day. Which code block accomplishes this task while minimizing potential compute costs?. preds.write.mode("append").saveAsTable("churn_preds"). preds.write.format("delta").save("/preds/churn_preds"). (preds.writeStream .outputMode('overwrite') .option('checkpointPath', '/_checkpoints/churn_preds') .start('/preds/churn_preds')). (preds.write .format('delta') .mode('overwrite') .saveAsTable('churn_preds')). (preds.writeStream .outputMode('append') .option('checkpointPath', '/_checkpoints/churn_preds') .start('churn_preds')).

An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable: spark.read .format('parquet') .load(f''/mnt/raw_orders/{date}") .dropDuplicates(['customer_id', 'order_id']) .write .mode('append') .saveAsTable('orders') ) Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order. If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will fail. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.

A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table. Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales. Cmd1 %python countries_af = [x[0] for x in spark.table('geo_lookup').filter(''continent = 'AF' ").select('country').collect()] Cmd2 %sql CREATE VIEW sales_af AS SELECT * FROM sales WHERE city IN countries_af AND CONTINENT = 'AF' Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?. Both commands will succeed. Executing show tables will show that countries_af and sales_af have been registered as views. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries_af: if this entity exists, Cmd 2 will succeed. Cmd 1 will succeed and Cmd 2 will fail. countries_af will be a Python variable representing a PySpark DataFrame. Both commands will fail. No new variables, tables, or views will be created. Cmd 1 will succeed and Cmd 2 will fail. countries_af will be a Python variable containing a list of strings.

A Delta table of weather records is partitioned by date and has the below schema: date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT To find all the records from within the Arctic Circle, you execute a query with the below filter: latitude > 66.3 Which statement describes how the Delta engine identifies which files to load?. All records are cached to an operational database and then the filter is applied. The Parquet file footers are scanned for min and max statistics for the latitude column. All records are cached to attached storage and then the filter is applied. The Delta log is scanned for min and max statistics for the latitude column. The Hive metastore is scanned for min and max statistics for the latitude column.

The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings. The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization. The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data. Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?. Because the VACUUM command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the VACUUM job is run the following day. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the VACUUM job is run 8 days later.

A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create. { 'name': 'Ingest new data', 'existing_cluster_id': '6015-954420-peace720', 'notebook_task': { 'notebook_path': '/Prod/ingest.py'} } Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.

An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id. For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour. Which solution meets these requirements?. Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state. Use MERGE INTO to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse. Ingest all log information into a bronze table; use MERGE INTO to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.

An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema: user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id. Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the described account_current table as part of each hourly batch job?. Use Auto Loader to subscribe to new files in the account_history directory; configure a Structured Streaming trigger once job to batch update newly detected files into the account_current table. Overwrite the account_current table with each batch using the results of a query against the account_history table grouping by user_id and filtering for the max value of last_updated. Filter records in account_history using the last_updated field and the most recent hour processed, as well as the max last_iogin by user_id write a merge statement to update or insert the most recent value for each user_id. Use Delta Lake version history to get the difference between the latest version of account_history and one version prior, then write these records to account_current. Filter records in account_history using the last_updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the most recent value for each username.

A table in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources. The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours. Which approach would simplify the identification of these changed records?. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model. Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions. Modify the overwrite logic to include a field populated by calling spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.

A table is registered with the following code: CREATE TABLE recent_orders AS ( SELECT a.user_id, a_email, b.order, b.order_date FROM (SELECT user_id, email FROM users) a INNER JOIN (SELECT user_id, order_id, order_date FROM orders WHERE order_date >= (current_date() - 7)) b ON a.user_id = b.user_id ) Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.

A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB. Which of the following likely explains these smaller file sizes?. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations. Z-order indices calculated on the table are preventing file compaction. Bloom filter indices calculated on the table are preventing file compaction. Databricks has autotuned to a smaller target file size based on the overall size of data in the table. Databricks has autotuned to a smaller target file size based on the amount of data in each partition.

Which statement regarding stream-static joins and static Delta tables is correct?. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization. The checkpoint directory will be used to track state information for the unique keys present in the join. Stream-static joins cannot use static Delta tables because of consistency issues. The checkpoint directory will be used to track updates to the static Delta table.

A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device. Streaming DataFrame df has the following schema: "device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT" Code block: df.withWatermark('event_time', '10 minutes') .groupBy( ____, 'device_id' ) .agg( avg('temp').alias('avg_temp'), avg) Choose the response that correctly fills in the blank within the code block to complete this task. to_interval("event_time", "5 minutes").alias("time"). window("event_time", "5 minutes").alias("time"). "event_time". window("event_time", "10 minutes").alias("time"). lag("event_time", "10 minutes").alias("time").

A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams. The proposed directory structure is displayed below: ./bronze _checkpoint _delta_log year_week=2020_01 year_week=2020_02 .... Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?. No; Delta Lake manages streaming checkpoints in the transaction log. Yes; both of the streams can share a single checkpoint directory. No; only one stream can write to a Delta Lake table. Yes; Delta Lake supports infinite concurrent writers. No; each of the streams needs to have its own checkpoint directory.

A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds. Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped. The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism. Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.

Which statement describes Delta Lake Auto Compaction?. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an OPTIMIZE job is executed toward a default of 1 GB. Before a Jobs cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an OPTIMIZE job is executed toward a default of 128 MB.

Which statement characterizes the general programming model used by Spark Structured Streaming?. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.

Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?. spark.sql.files.maxPartitionBytes. spark.sql.autoBroadcastJoinThreshold. spark.sql.files.openCostInBytes. spark.sql.adaptive.coalescePartitions.minPartitionNum. spark.sql.adaptive.advisoryPartitionSizeInBytes.

A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum. Which situation is causing increased duration of the overall job?. Task queueing resulting from improper thread pool assignment. Spill resulting from attached volume storage being too small. Network latency due to some cluster nodes being in different regions from the source data. Skew caused by more data being assigned to a subset of spark-partitions. Credential validation errors while pulling data from an external system.

Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM. Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?. • Total VMs; 1 • 400 GB per Executor • 160 Cores / Executor. • Total VMs: 8 • 50 GB per Executor • 20 Cores / Executor. • Total VMs: 16 • 25 GB per Executor • 10 Cores/Executor. • Total VMs: 4 • 100 GB per Executor • 40 Cores/Executor. • Total VMs:2 • 200 GB per Executor • 80 Cores / Executor.

A junior data engineer on your team has implemented the following code block. MERGE INTO events USING new_events ON events.event_id = new_events.event_id WHEN NOT MATCHED INSERT * The view new_events contains a batch of records with the same schema as the events Delta table. The event_id field serves as a unique key for this table. When this query is executed, what will happen with new records that have the same event_id as an existing record?. They are merged. They are ignored. They are updated. They are inserted. They are deleted.

A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job: from pyspark.sql.functions import col spark.read.format('delta') .option('readChangeFeed', true) .option('startingVersion', 0) .table('bronze') .filter(col('_change_type').isin(['update_postimage', 'insert'])) .write .mode('append') .table('bronze_history_type1') ) Which statement describes the execution and results of running the above query multiple times?. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table, giving the desired result.

A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source. That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months. Which describes how Delta Lake can help to avoid data loss of this nature in the future?. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance. Ingesting all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.

A nightly job ingests data into a Delta Lake table using the following code: from pyspark.sql.functions import current_timestamp, input_file_name, col from pyspark.sql.functions import Column def ingest_daily_batch(time_col: Column, year:int, month:int, day:int): (spark.read .format('parquet') .load("/mnt/daily_batch/(year)/(month)/(day)") .select("*", time_col.alias("ingest_time"), inpute_file_name().alias("source_file") ) .write .mode("append") .saveAsTable("bronze") ) The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline. Which code snippet completes this function definition? def new_records(): return spark.readStream.table("bronze"). return spark.readStream.load("bronze"). return (spark.read .table("bronze") .filter(col("ingets_time") == current_timestamp()) ). return spark.read.option("readChangeFeed", "true").table ("bronze"). return (spark.read .table("bronze") .filter(col("source_file") == f"/mnt/daily_batch/(year)/(month)/(day)") ).

A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure. The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications. The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields. Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?. The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place. Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads. Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

The data engineering team maintains the following code: accountDF = spark.table("accounts") orderDF = spark.table("orders") itemDF = spark.table("items") orderWithItemDF = (orderDF.join( itemDF, orderDF.itemID == itemDF.itemID) .select( orderDF.accountID, orderDF.itemID, itemDF.itemName)) finalDF = (accountDF.join( orderWithItemDF, accountDF.accountID == orderWithItemDF.accountID) .select( orderWithItemDF["*"], accountDF.city)) (finalDF.write .mode("overwrite") .table("enriched_itemized_orders_by_account")) Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.

The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels. The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams. Which statement exemplifies best practices for implementing this system?. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse. Storing all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.

The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables. Which approach will ensure that this requirement is met?. Whenever a database is being created, make sure that the LOCATION keyword is used. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT. Whenever a table is being created, make sure that the LOCATION keyword is used. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement. When the workspace is being configured, make sure that external cloud object storage has been mounted.

To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries. The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added. Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.

A Delta Lake table representing metadata about content posts from users has the following schema: user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE This table is partitioned by the date column. A query is run with the following filter: longitude < 20 & longitude > -20 Which statement describes how data will be filtered?. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.

A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States. The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed. Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near. Databricks notebooks send all executable code from the user’s browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.

The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes. A junior engineer has written the following code to add CHECK constraints to the Delta Lake table: ALTER TABLE activity_details ADD CONSTRAINT valid_coordinates CHECK ( latitude >= -90 AND latitude <= 90 AND longitude >= -180 AND longitude <= 180) A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed. Which statement explains the cause of this failure?. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing. The activity_details table already exists; CHECK constraints can only be added during initial table creation. The activity_details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table. The activity_details table already contains records; CHECK constraints can only be added prior to inserting values into a table. The current table schema does not contain the field valid_coordinates; schema evolution will need to be enabled before altering the table to add a constraint.

Which of the following is true of Delta Lake and the Lakehouse?. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table. Z-order can only be applied to numeric values stored in Delta Lake tables.

The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table. The following logic is used to process these records. MERGE INTO customers USING ( SELECT updates.customer_id as merge_ey, updates.* FROM updates UNION ALL SELECT NULL as merge_key, updates.* from updates JOIN customers ON updates.customer_id = customers.customer_id WHERE customers.current = true AND customers.address <> staged_updates.address THEN WHEN NOT MATCHED THEN INSERT(customer_id, address, current, effective_date, end_date) VALUES(staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) ) Which statement describes this implementation?. The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.

The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?. Can Manage. Can Edit. No permissions. Can Read. Can Run.

A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs. The user_ltv table has the following schema: email STRING, age INT, ltv INT The following view definition is executed: CREATE VIEW email_ltv AS SELECT CASE WHEN is_member('marketing') THEN email ELSE 'REDACTED' END AS email, ltv FROM user_ltv An analyst who is not a member of the marketing group executes the following query: SELECT * FROM email_ltv - Which statement describes the results returned by this query?. Three columns will be returned, but one column will be named "REDACTED" and contain only null values. Only the email and ltv columns will be returned; the email column will contain all null values. The email and ltv columns will be returned with the values in user_ltv. The email.age, and ltv columns will be returned with the values in user_ltv. Only the email and ltv columns will be returned; the email column will contain the string "REDACTED" in each row.

Question #43Topic 1 The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true. The following SQL DDL statement is executed to create a new table: CREATE TABLE dev.pii_test (id INT, name STRING COMMENT "PII") COMMENT "Contains PII" TBLPROPERTIES ('contains_pii' = True ) Which command allows manual confirmation that these three requirements have been met?. DESCRIBE EXTENDED dev.pii_test. DESCRIBE DETAIL dev.pii_test. SHOW TBLPROPERTIES dev.pii_test. DESCRIBE HISTORY dev.pii_test. SHOW TABLES dev.

The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users. DELETE FROM users WHERE user_id N (SELECT user_id FROM delete_requests) Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?. Yes; Delta Lake ACID guarantees provide assurance that the DELETE command succeeded fully and permanently purged these records. No; the Delta cache may return records from previous versions of the table until the cluster is restarted. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk. No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command. No; files containing deleted records may still be accessible with time travel until a VACUUM command is used to remove invalidated data files.

An external object storage container has been mounted to the location /mnt/finance_eda_bucket. The following logic was executed to create a database for the finance team: CREATE DATABASE finance_eda_db LOCATION '/mnt/finance_eda_bucket'; GRANT USAGE ON DATABASE finance_eda_db TO finance; GRANT CREATE ON DATABASE finance_eda_db TO finance; After the database was successfully created and permissions configured, a member of the finance team runs the following code: CREATE TABLE finance_eda_db.tx_sales AS SELECT * FROM sales WHERE state = "TX"; If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane. An external table will be created in the storage container mounted to /mnt/finance_eda_bucket. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane. An managed table will be created in the storage container mounted to /mnt/finance_eda_bucket. A managed table will be created in the DBFS root storage container.

Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets. Which statement describes a limitation of Databricks Secrets?. Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display the value in plain text. Account administrators can see all secrets in plain text by logging on to the Databricks Accounts console. Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default. Iterating through a stored secret and printing each character will display secret contents in plain text. The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.

What statement is true regarding the retention of job run history?. It is retained until you export or delete job run logs. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3. It is retained for 60 days, during which you can export notebook run results to HTML. It is retained for 60 days, after which logs are archived. It is retained for 90 days or until the run-id is re-used through custom run configuration.

A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. Which statement describes the contents of the workspace audit logs concerning these events?. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identify these events. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.

A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively. Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs, all PySpark and Spark SQL logic should be refactored. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production. Calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. The Jobs UI should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.

A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor. When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?. The five Minute Load Average remains consistent/flat. Bytes Received never exceeds 80 million bytes per second. Total Disk Space remains constant. Network I/O never spikes. Overall cluster CPU utilization is around 25%.

Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?. In the Executor’s log file, by grepping for "predicate push-down". In the Stage’s Detail screen, in the Completed Stages table, by noting the size of data read from the Input column. In the Storage Detail screen, by noting which RDDs are not stored on disk. In the Delta Lake transaction log. by noting the column statistics. In the Query Detail screen, by interpreting the Physical Plan.

Review the following error traceback: Which statement describes the error being raised?. The code executed was PySpark but was executed in a Scala notebook. There is no column in the table named heartrateheartrateheartrate. There is a type error because a column object cannot be multiplied. There is a type error because a DataFrame object cannot be multiplied. There is a syntax error because the heartrate column is not correctly identified as a column.

Which distribution does Databricks support for installing custom Python code packages?. sbt. CRANC. npm. Wheels. jars.

Which Python variable contains a list of directories to be searched when trying to locate required modules?. importlib.resource_path. sys.path. os.path. pypi.path. pylib.source.

Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code. Which statement describes a main benefit that offset this additional effort?. Improves the quality of your data. Validates a complete use case of your application. Troubleshooting is easier since all steps are isolated and tested individually. Yields faster deployment and execution times. Ensures that all steps interact correctly to achieve the desired end result.

Which statement describes integration testing?. Validates interactions between subsystems of your application. Requires an automated testing framework. Requires manual intervention. Validates an application use case. Validates behavior of individual elements of your application.

Which REST API call can be used to review the notebooks configured to run as tasks in a multi-task job?. /jobs/runs/list. /jobs/runs/get-output. /jobs/runs/get. /jobs/get. /jobs/list.

A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A. If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure. All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.

A Delta Lake table was created with the below query: CREATE TABLE prod.sales_by_stor USING DELTA LOCATION "/mnt/prod/sales_by_store" Realizing that the original query had a typographical error, the below code was executed: ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store Which result will occur after running the second command?. The table reference in the metastore is updated and no data is changed. The table name change is recorded in the Delta transaction log. All related files and metadata are dropped and recreated in a single ACID transaction. The table reference in the metastore is updated and all data files are moved. A new Delta transaction log Is created for the renamed table.

The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows: store_id INT, total_Sales_wtd FLOAT, avg_daily_sales_qtd FLOAT, total_sales_ytd FLOAT, avg_daily_sales_ytd FLOAT, previous_day_sales FLOAT, total_Sales_7d FLOAT, avg_Saily_sales_7d FLOAT, updated TIMESTAMP The table daily_store_sales contains all the information needed to update store_sales_summary. The schema for this table is: store_id INT, sales_date DATE, total_sales FLOAT If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.