Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER Online Practice Questions and Exam Preparation

Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER Online Questions & Answers

• Question 31:

  The data architect has decided that once data has been ingested from external sources into the

  Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.

  The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.

  GRANT USAGE ON DATABASE prod TO eng;

  GRANT SELECT ON DATABASE prod TO eng;

  Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

  A. Group members have full permissions on the prod database and can also assign permissions to other users or groups.
  B. Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.
  C. Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.
  D. Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.
  E. Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.
  D. Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.

• Question 32:

  A data engineer has created a new cluster using shared access mode with default configurations. The data engineer needs to allow the development team access to view the driver logs if needed. What are the minimal cluster permissions that allow the development team to accomplish this?

  A. CAN ATTACH TO
  B. CAN MANAGE
  C. CAN VIEW
  D. CAN RESTART
  C. CAN VIEW

• Question 33:

  A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.

  In which location can one review the timeline for cluster resizing events?

  A. Workspace audit logs
  B. Driver's log file
  C. Ganglia
  D. Cluster Event Log
  E. Executor's log file
  D. Cluster Event Log

• Question 34:

  A junior data engineer on your team has implemented the following code block.

  

  The viewnew_eventscontains a batch of records with the same schema as theeventsDelta table. Theevent_idfield serves as a unique key for this table.

  When this query is executed, what will happen with new records that have the sameevent_idas an existing record?

  A. They are merged.
  B. They are ignored.
  C. They are updated.
  D. They are inserted.
  E. They are deleted.
  B. They are ignored.

• Question 35:

  The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. Therecent_sensor_recordingstable contains an identifyingsensor_idalongside thetimestampandtemperaturefor the most recent 5 minutes of recordings.

  The below query is used to create the alert:

  

  The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger whenmean (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?

  A. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
  B. Therecent_sensor_recordingstable was unresponsive for three consecutive runs of the query
  C. The source query failed to update properly for three consecutive minutes and then restarted
  D. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
  E. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
  E. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query

• Question 36:

  The data science team has created and logged a production using MLFlow. The model accepts a list of column names and returns a new column of type DOUBLE.

  The following code correctly imports the production model, load the customer table containing the customer_id key column into a Dataframe, and defines the feature columns needed for the model.

  

  Which code block will output DataFrame with the schema'' customer_id LONG, predictions DOUBLE''?

  A. Model, predict (df, columns)
  B. Df, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')
  C. Df. Select (''customer_id''. Model (''columns) alias (''predictions'')
  D. Df.apply(model, columns). Select (''customer_id, prediction''
  A. Model, predict (df, columns)

• Question 37:

  A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.

  A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays:

  Which limitation will the team face while diagnosing this problem?

  A. New fields not be computed for historic records.
  B. Updating the table schema will invalidate the Delta transaction log metadata.
  C. Updating the table schema requires a default value provided for each file added.
  D. Spark cannot capture the topic partition fields from the kafka source.
  A. New fields not be computed for historic records.

• Question 38:

  A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks. In the code below, Impressions is a streaming DataFrame with a watermark ("event_time", "10 minutes") Which solution would improve the performance?

  

  A. Joining on event time constraint: clickTime >= impressionTime AND clickTime
  B. Joining on event time constraint: clickTime + 3 hours < impressionTime - 2 hours
  C. Joining on event time constraint: clickTime == impressionTime using a leftOuter join
  D. Joining on event time constraint: clickTime >= impressionTime - interval 3 hours and removing watermarks
  A. Joining on event time constraint: clickTime >= impressionTime AND clickTime

• Question 39:

  Review the following error traceback: Which statement describes the error being raised?

  

  A. The code executed was PvSoark but was executed in a Scala notebook.
  B. There is no column in the table named heartrateheartrateheartrate
  C. There is a type error because a column object cannot be multiplied.
  D. There is a type error because a DataFrame object cannot be multiplied.
  E. There is a syntax error because the heartrate column is not correctly identified as a column.
  B. There is no column in the table named heartrateheartrateheartrate

• Question 40:

  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?

  A. Task queueing resulting from improper thread pool assignment.
  B. Spill resulting from attached volume storage being too small.
  C. Network latency due to some cluster nodes being in different regions from the source data
  D. Skew caused by more data being assigned to a subset of spark-partitions.
  E. Credential validation errors while pulling data from an external system.
  D. Skew caused by more data being assigned to a subset of spark-partitions.