DE-ASSOC practice questions 121–130

1. Question 121 · 170 in bank

   A data engineer has inherited a Databricks pipeline from a previous team. The pipeline is missing SLAs and costs more than the allotted budget. On analysis, it is noted that the cluster is not being fully utilized, and the dataset is getting skewed. How should the data engineer resolve this issue?

   • AUse coalesce() on the dataset to merge partitions and reduce skew.
   • BIncrease the number of executors for the job.
   • CRepartition the dataset to have it be more optimally spread across all nodes.
   • DIncrease the executor memory for the job.

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   Answer: C

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2. Question 122 · 170 in bank

   A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE. The table is configured to run in Development mode using the Continuous Pipeline Mode. Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

   • AAll datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
   • BAll datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.
   • CAll datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
   • DAll datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
   • EAll datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.

   Show answer

   Answer: E

   Explanation

   Ans E : Development and production modes You can optimize pipeline execution by switching between development and production modes. Use the Delta Live Tables Environment Toggle Icon buttons in the Pipelines UI to switch between these two modes. By default, pipelines run in development mode. When you run your pipeline in development mode, the Delta Live Tables system does the following: Reuses a cluster to avoid the overhead of restarts. By default, clusters run for two hours when development mode is enabled. You can change this with the pipelines.clusterShutdown.delay setting in the Configure your compute settings. Disables pipeline retries so you can immediately detect and fix errors. In production mode, the Delta Live Tables system does the following: Restarts the cluster for specific recoverable errors, including memory leaks and stale credentials. Retries execution in the event of specific errors, for example, a failure to start a cluster. https://docs.databricks.com/en/delta-live-tables/updates.html#optimize-execution

   Source: Adapted from community discussion — meow_akk

   References

   • Databricks documentation
   • Databricks documentation

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3. Question 123 · 170 in bank

   An organization is looking for an optimized storage layer that supports ACID transactions and schema enforcement. Which technology should the organization use?

   • ADelta Lake
   • BUnity Catalog
   • CCloud File Storage
   • DData lake

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   Answer: A

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4. Question 124 · 170 in bank

   Which of the following data workloads will utilize a Gold table as its source?

   • AA job that enriches data by parsing its timestamps into a human-readable format
   • BA job that aggregates uncleaned data to create standard summary statistics
   • CA job that cleans data by removing malformatted records
   • DA job that queries aggregated data designed to feed into a dashboard
   • EA job that ingests raw data from a streaming source into the Lakehouse

   Show answer

   Answer: D

   References

   • Databricks documentation

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5. Question 125 · 170 in bank

   What are the transformations typically included in building the Bronze layer?

   • AInclude columns Load date/time, process ID
   • BBusiness rules and transformations
   • CPerform extensive data cleansing
   • DAggregate data from multiple sources

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   Answer: A

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6. Question 126 · 170 in bank

   An organization has data stored across multiple external systems, including MySQL, Amazon Redshift, and Google BigQuery. The data engineer wants to perform analytics without ingesting directly into Databricks, ensuring unified governance and minimizing data duplication. Which feature of Databricks enables querying these external data sources while maintaining centralized governance?

   • ADelta Lake
   • BLakehouse Federation
   • CMLflow
   • DDatabricks Connect

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   Answer: B

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7. Question 127 · 170 in bank

   Which of the following describes the type of workloads that are always compatible with Auto Loader?

   • AStreaming workloads
   • BMachine learning workloads
   • CServerless workloads
   • DBatch workloads
   • EDashboard workloads

   Show answer

   Answer: A

   References

   • Databricks documentation

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8. Question 128 · 170 in bank

   A data engineer streams customer orders into a Kafka topic (orders_topic) and is currently writing the ingestion script of a DLT pipeline. The data engineer needs to ingest the data from Kafka brokers to DLT using Databricks. What is the correct code for ingesting the data?

   • A//IMG//
   • B//IMG//
   • C//IMG//
   • D//IMG//

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   Answer: A

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9. Question 129 · 170 in bank

   A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

   

   Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

   • AReplace predict with a stream-friendly prediction function
   • BReplace schema(schema) with option ("maxFilesPerTrigger", 1)
   • CReplace "transactions" with the path to the location of the Delta table
   • DReplace format("delta") with format("stream")
   • EReplace spark.read with spark.readStream

   Show answer

   Answer: E

   References

   • Databricks documentation
   • Databricks documentation

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10. Question 130 · 170 in bank

    A data engineer is attempting to write Python and SQL in the same command cell and is running into an error. The engineer thought that it was possible to use a Python variable in a select statement. Why does the command fail?

    • ADatabricks supports language interoperability in the same cell but only between Scala and SQL.
    • BDatabricks supports multiple languages but only one per notebook.
    • CDatabricks supports one language per cell.
    • DDatabricks supports language interoperability but only if a special character is used.

    Show answer

    Answer: C

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DE-ASSOC practice, question 121. 170 questions available.