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Free CERTIFIED-DATA-ENGINEER-PROFESSIONAL – Databricks CERTIFIED-DATA-ENGINEER-PROFESSIONAL Practice Test Question

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Showing 7–9 of 26 questions

Question 7 (Topic 1)

The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows: The table daily_store_sales contains all the information needed to update store_sales_summary. The schema for this table is: store_id INT, sales_date DATE, total_sales FLOAT If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

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  • ○ Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
  • ○ Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
  • ○ Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
  • ○ Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
  • ○ Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.
Question 8 (Topic 1)

A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new promotion, and they would like to add a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows. Note that proposed changes are in bold. Original query: Proposed query: Proposed query: .start(“/item_agg”) Which step must also be completed to put the proposed query into production?

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  • ○ Specify a new checkpointLocation
  • ○ Increase the shuffle partitions to account for additional aggregates
  • ○ Run REFRESH TABLE delta.'/item_agg'
  • ○ Register the data in the "/item_agg" directory to the Hive metastore
  • ○ Remove .option(‘mergeSchema’, ‘true’) from the streaming write
Question 9 (Topic 1)

A data pipeline uses Structured Streaming to ingest data from Apache Kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka-generated timestamp, key, and value. Three months after the pipeline is deployed, the data engineering team has noticed some latency issues 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 recorded by Apache Spark) as well as the Kafka topic and partition. The team plans to use these additional metadata fields to diagnose the transient processing delays. Which limitation will the team face while diagnosing this problem?

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  • ○ New fields will not be computed for historic records.
  • ○ Spark cannot capture the topic and partition fields from a Kafka source.
  • ○ New fields cannot be added to a production Delta table.
  • ○ Updating the table schema will invalidate the Delta transaction log metadata.
  • ○ Updating the table schema requires a default value provided for each field added.