DE-PRO practice questions 181–190

1. Question 181 · 202 in bank

   A data engineer is designing a system leveraging Lakeflow Declarative Pipeline technology to process real-time truck telemetry data ingested from JSON files in S3 using Auto Loader. The data includes truck_id, timestamp, location, speed, and fuel_level. The system must support two use cases: 1. Near-real-time monitoring of the latest location, speed, and fuel_level per truck_id for the operations team. 2. Daily aggregated reports of total distance traveled and average fuel efficiency per truck_id for the management team. Which approach should the data engineer use for streaming tables and materialized views in the Lakeflow Declarative Pipeline to meet these requirements?

   • ADefine a streaming table to ingest and store the raw telemetry data, and create a streaming table to compute the daily aggregated distance and fuel efficiency per truck_id reporting. Create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
   • BDefine a streaming table to ingest and store the raw telemetry data, and create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring. Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
   • CDefine a streaming table to ingest and store the raw telemetry data, and create a streaming table to incrementally compute the latest location, speed, and fuel_level per truck_id for real-time monitoring. Create a materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
   • DDefine a materialized view to ingest and store the raw telemetry data, and create a streaming table to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring. Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.

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   Answer: C

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2. Question 182 · 202 in bank

   A data engineering team is setting up a Git project to automate integration tests using Databricks Asset Bundles and the Git provider’s CI/CD functionalities. When a pull containing changes to their pipleline is sent, they need to run a Job to test their data pipeline. What is the correct databricks bundle command sequence to be executed from the Git provider’s CI/CD automation for this task?

   • Ainit, deploy, run, validate
   • Bvalidate, deploy, run
   • Cinit, validate, deploy, run
   • Ddeploy, run, validate

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   Answer: B

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3. Question 183 · 202 in bank

   A data engineer is attempting to execute the following PySpark code: df=spark.read.table(“sales”) result=df.groupBy(“region”).agg(sum(“revenue”)) However, upon inspecting the execution plan and profiling the Spark job, they observe excessive data shuffling during the aggregation phase. Which technique should be applied to reduce shuffling during the groupBy aggregation operation?

   • AUse.coalesce(1) after the aggregation
   • BCaching the DataFrame df
   • CUse broadcast join
   • DRepartition by region before aggregation

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   Answer: D

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4. Question 184 · 202 in bank

   When a new Databricks project starts, the central IP team provisions the required infrastructure using Terraform and a Service Principal. This includes creating a Databricks workspace, a Unity Catalog linked to an External Location, and a Databricks group containing all project team members. Project teams must store all assets – e.g., tables and volumes, as Managed assets in Unity Catalog. This model hides infrastructure complexity while giving teams autonomy within their catalog. They can create and manage schemas, tables, volumes, and related objects but cannot rename, delete, or change catalog permissions, those remain under IT’s control. Which rights should the project group be granted to enable this model?

   • AThe group needs to have USE CATALOG and USE SCHEMA on the catalog.
   • BThe group needs to have ALL PRIVILEGES and the MANAGE on the catalog.
   • CThe group needs to have ALL PRIVILEGES on the catalog.
   • DThe group should be made OWNER of the catalog.

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   Answer: C

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5. Question 185 · 202 in bank

   Select 2

   A data engineer needs to productionize a new Spark application written by teammate. This application has numerous external dependencies, including libraries, and requires custom environment variables and Spark configuration parameters to be set. Which two methods will help the data engineer accomplish the task? (Choose two.)

   • AInstall libraries on DBFS
   • BAdd libraries to compute policies
   • CUse secrets in init scripts to store configuration data
   • DUse compute policies to set system properties, environment variables, and Spark configuration parameters.
   • ECreate init scripts on DBFS.

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   Answer: BD

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6. Question 186 · 202 in bank

   A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data. Which configuration will provide the optimal performance while enabling these capabilities?

   • AUse the open sharing protocol instead of Databricks-to Databricks sharing for better performance.
   • BShare tables WITHOUT HISTORY and enable partitioning for better query performance.
   • CShare tables WITH HISTORY, ensure tables don't have partitioning enabled. and enable CDF before sharing.
   • DShare the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.

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   Answer: C

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7. Question 187 · 202 in bank

   A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog-enabled workspace. The data engineering team is sharing large Delta tables with an internal partner via Databricks-to-Databricks and aggregated reports with an external client via Open Sharing. While testing new sharing workflows, the data engineering team encounters challenges related to access control, data update visibility, and shareable object types. What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to-Databricks or Open Sharing?

   • ADelta Sharing does not support Unity Catalog enabled tables; only legacy Hive Metastore tables are shareable.
   • BWith Databricks-to-Databricks sharing. Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.
   • CDelta Sharing (both Databricks-to-Databricks and Open sharing) allows recipients to modify the source data if they have SELECT privileges.
   • DWith Open sharing, recipients cannot access Volumes, Models, or notebooks — only static Delta tables are supported.

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   Answer: D

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8. Question 188 · 202 in bank

   A data engineer is creating a daily reporting Job. There are two reporting notebooks one for weekdays and the other for weekends. An "If/else condition" task is configured as "{{job.start_time.is_weekday}} == true" to route the job to either weekday or weekend notebook tasks. The same job would be used in multiple time zones. Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?

   • AReject. As they should use {{job.trigger_time.is_weekday}} instead.
   • BReject. As the {{job.start_time.is_weekday}} is not a valid value reference.
   • CReject. As the {{job.start_time.is_weekday}} is for the UTC timezone.
   • DMerge. As the job configuration looks good.

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   Answer: C

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9. Question 189 · 202 in bank

   A data engineer is configuring a Lakeflow Spark Declarative Pipelines to process CDC data from a source. The source events sometimes arrive out of order, and multiple updates may occur with the same update_timestamp but with different update_sequence_id. What should the data engineer do to ensure events are sequenced correctly?

   • AUse SEQUENCE BY STRUCT(event_timestamp, update_sequence_id) in AUTO CDC APIs.
   • BSet track history column list to [event timestamp, event id] in AUTO CDC APIs.
   • CUse dropduplicate() to remove out-of-order and duplicate records in LDP.
   • DUse a window function to sort update_sequence_id within the same partition, i.e., update_timestamp in the LDP pipeline.

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   Answer: A

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10. Question 190 · 202 in bank

    A data engineer is designing a data pipeline in Databricks that needs to process records from a Kafka stream where late-arriving data common. Which approach should the data engineer use?

    • AUse a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.
    • BUse an Auto CDC pipeline with batch tables to simplify late data handling.
    • CImplement a custom solution using Databricks Jobs to periodically reprocess all historical data
    • DUse batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.

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    Answer: A

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DE-PRO practice, question 181. 202 questions available.