Free GAQM Databricks-Certified-Data-Engineer-Associate Exam Dumps Questions & Answers

A data engineer has multiple Unity Catalog-managed Delta tables that require regular maintenance. Currently, the data engineer manually schedules OPTIMIZE and VACUUM jobs for each table, adjusting the frequency based on how often each table is queried and updated. The engineer needs to eliminate this manual maintenance overhead and have the platform automatically determine when and how to run these operations. Which action should the engineer take to meet the requirements?

• A.  Set the delta.autoOptimize.autoCompact and delta.autoOptimize.optimizeWrite table properties to trigger automatic VACUUM and file compaction.
• B.  Enable Liquid Clustering with CLUSTER BY on each table to automatically compact and remove stale data files on a scheduled basis.
• C.  Enable predictive optimization at the account, catalog, or schema level so the platform automatically identifies and runs OPTIMIZE and VACUUM on managed tables.
• D.  Configure Auto Loader with schema evolution enabled to automatically trigger OPTIMIZE and VACUUM after each data ingestion batch completes.

A data engineer needs to ingest streaming data from Kafka into a Delta Lake table using Databricks. The pipeline must guarantee exactly-once processing semantics and maintain checkpoint information to recover from failures. Which Spark Structured Streaming feature ensures reliable processing and fault tolerance?

• A.  Watermarking
• B.  Checkpointing
• C.  Partition pruning
• D.  Caching

Identify how the count_if function and the count where x is null can be used.
Consider a table random_values with below data.
What would be the output of below query?
select count_if(col1 > 1) as count_a, count(*) as count_b,count(col1)
as count_c from random_values
col1
0
1
2
NULL
2
3

• A.  4 6 6
• B.  4 6 5
• C.  3 6 5
• D.  3 6 6

A data engineer wants a Structured Streaming job to process all data available in the source, then stop automatically, so it can run on a scheduled job cluster instead of running continuously.
Which trigger should be configured?

• A.  .trigger(continuous="1 second")
• B.  .trigger(processingTime="0 seconds")
• C.  .trigger(availableNow=True)
• D.  .trigger(once=True) with maxFilesPerTrigger disabled

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?

• A.  region_sales = sales_df.groupBy("category").sum("sales_amount").alias("total_sales_amount")
• B.  region_sales = sales_df.groupBy("region").agg(sum("sales_amount").alias("total_sales_amount"))
• C.  region_sales = sales_df.sum("sales_amount").groupBy("region").alias("total_sales_amount")
• D.  region_sales = sales_df.agg(sum("sales_amount").groupBy("region").alias("total_sales_amount"))