DE-ASSOC practice questions 131–140

1. Question 131 · 170 in bank

   Which of the following Git operations must be performed outside of Databricks Repos?

   • ACommit
   • BPull
   • CPush
   • DClone
   • EMerge

   Show answer

   Answer: E

   Explanation

   wrong answer, Clone can be done in Databricks Repo. Merge not in Repos, need to be in Git. Link here: https://learn.microsoft.com/en-us/azure/databricks/repos/

   Source: Adapted from community discussion — upliftinghut

   References

   • Databricks documentation
   • Databricks documentation
   • Databricks documentation
   • Databricks documentation
   • Microsoft Learn

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2. Question 132 · 170 in bank

   Which compute option should be chosen in a scenario where small-scale ad-hoc Python scripts need to be run at high frequency and should wind down quickly after these queries have finished running?

   • AAll-purpose Cluster
   • BJob Cluster
   • CServerless Compute
   • DSQL Warehouse

   Show answer

   Answer: C

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3. Question 133 · 170 in bank

   A dataset has been defined using Delta Live Tables and includes an expectations clause: CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE What is the expected behavior when a batch of data containing data that violates these constraints is processed?

   • ARecords that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
   • BRecords that violate the expectation cause the job to fail.
   • CRecords that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
   • DRecords that violate the expectation are added to the target dataset and recorded as invalid in the event log.
   • ERecords that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.

   Show answer

   Answer: B

   Explanation

   Ans B : delta live tables data quality expectations . - https://docs.databricks.com/en/delta-live-tables/expectations.html Action Result warn (default) Invalid records are written to the target; failure is reported as a metric for the dataset. drop Invalid records are dropped before data is written to the target; failure is reported as a metrics for the dataset. fail Invalid records prevent the update from succeeding. Manual intervention is required before re-processing.

   Source: Adapted from community discussion — meow_akk

   References

   • Databricks documentation
   • Databricks documentation

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4. Question 134 · 170 in bank

   Which of the following statements regarding the relationship between Silver tables and Bronze tables is always true?

   • ASilver tables contain a less refined, less clean view of data than Bronze data.
   • BSilver tables contain aggregates while Bronze data is unaggregated.
   • CSilver tables contain more data than Bronze tables.
   • DSilver tables contain a more refined and cleaner view of data than Bronze tables.
   • ESilver tables contain less data than Bronze tables.

   Show answer

   Answer: D

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5. Question 135 · 170 in bank

   A company sells products across multiple categories (e.g., Electronics, Clothing) and regions. The sales team has provided you with a PySpark dataframe named sales_df as below, and the team wants the data engineer to analyze the sales data to help make strategic decisions.

   

   Calculate the total sales amount for each region and store the results in a new dataframe called region_sales. Given the expected result:

   

   Which code will generate the expected result?

   • Aregion_sales = sales_df.groupBy("category").sum("sales_amount").alias("total_sales_amount")
   • Bregion_sales = sales_df.groupBy("region").agg(sum("sales_amount").alias("total_sales_amount"))
   • Cregion_sales = sales_df.sum("sales_amount").groupBy("region").alias("total_sales_amount")
   • Dregion_sales = sales_df.agg(sum("sales_amount").groupBy("region").alias("total_sales_amount"))

   Show answer

   Answer: B

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6. Question 136 · 170 in bank

   A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved. Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?

   • AThey can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to "Reliability Optimized."
   • BThey can turn on the Auto Stop feature for the SQL endpoint.
   • CThey can increase the cluster size of the SQL endpoint.
   • DThey can turn on the Serverless feature for the SQL endpoint.
   • EThey can increase the maximum bound of the SQL endpoint's scaling range.

   Show answer

   Answer: D

   Explanation

   The important point of this scenario is "when they are submitted to a non-running SQL endpoint". So its not about increasing the instance size or the amount of instances to improve the query performance, but its about reducing the start-up time. A: Not possible, serverless can't be combined with spot instance policies, see https://docs.databricks.com/en/compute/sql-warehouse/serverless.html#limitations B: Auto Stop is about terminating a SQL warehouse after x minutes of being idle. C: Increasing the cluster size provides more capacities for running queries, but doesn't reduce start-up time. D: Serverless reduces start-up time from minutes to seconds. Jackpot! E: Increasing the max bound of the SQL endpoints scaling range will help with lots of sequencial queries, which is not the case here.

   Source: Adapted from community discussion — carpa_jo

   References

   • Databricks documentation

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7. Question 137 · 170 in bank

   A data engineering team is using Kafka to capture event data and then ingest it into Databricks. The team wants to be able to see these historical events. Medallion architecture is already in place. The team wants to be mindful of costs. Where should this historical event data be stored?

   • AGold
   • BSilver
   • CBronze
   • DRaw layer

   Show answer

   Answer: C

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8. Question 138 · 170 in bank

   Which of the following approaches should be used to send the Databricks Job owner an email in the case that the Job fails?

   • AManually programming in an alert system in each cell of the Notebook
   • BSetting up an Alert in the Job page
   • CSetting up an Alert in the Notebook
   • DThere is no way to notify the Job owner in the case of Job failure
   • EMLflow Model Registry Webhooks

   Show answer

   Answer: B

   Explanation

   In Databricks, you can configure job notifications directly from the Jobs page, where you can specify that an email should be sent to the Job owner or other specified individuals in the case of Job failure. This is the most straightforward and automated way to ensure notifications are sent.

   Source: Adapted from community discussion — 80370eb

   References

   • Databricks documentation

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9. Question 139 · 170 in bank

   What is the maximum output supported by a job cluster to ensure a notebook does not fail?

   • A25MBs
   • B10MBs
   • C30MBs
   • D15MBs

   Show answer

   Answer: C

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10. Question 140 · 170 in bank

    Select 2

    Which two items are characteristics of the Gold Layer? (Choose two.)

    • AHistorical lineage
    • BRaw Data
    • CNormalised
    • DDe-normalised
    • ERead-optimized

    Show answer

    Answer: DE

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DE-ASSOC practice, question 131. 170 questions available.