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Databricks Certified Data Engineer Professional Sample Questions:

1. A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A) Databricks has autotuned to a smaller target file size based on the overall size of data in the table
B) Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
C) Databricks has autotuned to a smaller target file size based on the amount of data in each partition
D) Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations

2. A data engineer needs to design an efficient pipeline that automatically processes new CSV files as they arrive in S3 storage. Which Databricks feature should the data engineer use to meet these requirements?

A) Auto Loader with schema inference and evolution enabled
B) COPY INTO SQL command with parameters to track processed files
C) Traditional batch processing with scheduled Databricks Jobs
D) Streaming from cloud storage using standard Spark readStream with format ("csv") and format ("json")

3. A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete. Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?

A) Use Delta Lake's DESCRIBE HISTORY command to analyze table evolution and infer column purposes from historical changes.
B) Navigate to the table in Databricks Catalog Explorer, select the table schema view, and use the AI Generate option which leverages artificial intelligence to automatically create meaningful column descriptions based on column names, data types, sample values, and data patterns.
C) Use the DESCRIBE TABLE command to extract existing schema information and manually write descriptions based on column names and data types.
D) Write custom PySpark code using df.describe() and df.schema to programmatically generate basic statistical descriptions for each column.

4. An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema:
user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id.
Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the described account_current table as part of each hourly batch job?

A) Filter records in account history using the last updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the most recent value for each username.
B) Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.
C) Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.
D) Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent value for each user id.
E) Overwrite the account current table with each batch using the results of a query against the account history table grouping by user id and filtering for the max value of last updated.

5. The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

A) The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
B) The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
C) The source query failed to update properly for three consecutive minutes and then restarted
D) The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
E) The total average temperature across all sensors exceeded 120 on three consecutive executions of the query

Solutions: