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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

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

2. A Data Engineer is building a fraud detection pipeline that calls out to Open AI, via a Python library, and needs to include an access token when using the API. Which Databricks CLI command should the Data Engineer use to create the secret?

A) databricks secrets put-secret SCOPE KEY; dbutils.secrets.get (SCOPE, KEY)
B) databricks secrets put-secret KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)
C) databricks tokens put-token SCOPE KEY; dbutils.tokens.get (SCOPE, KEY)
D) databricks tokens put-token KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)

3. A data team is implementing an append-only Delta Lake pipeline that processes both batch and streaming data. They want to ensure that schema changes in the source data are automatically incorporated without breaking the pipeline. Which configuration should the team use when writing data to the Delta table?

A) ignoreChanges = false
B) validateSchema = false
C) mergeSchema = true
D) overwriteSchema = true

4. A junior data engineer on your team has implemented the following code block.

The view new_events contains a batch of records with the same schema as the events Delta table. The event_id field serves as a unique key for this table.
When this query is executed, what will happen with new records that have the same event_id as an existing record?

A) They are ignored.
B) They are updated.
C) They are deleted.
D) They are inserted.
E) They are merged.

5. A data engineering team has a time-consuming data ingestion job with three data sources. Each notebook takes about one hour to load new data. One day, the job fails because a notebook update introduced a new required configuration parameter. The team must quickly fix the issue and load the latest data from the failing source. Which action should the team take?

A) Repair the run with the new parameter.
B) Share the analysis with the failing notebook owner so that they can fix it quickly.
C) Update the task by adding the missing task parameter, and manually run the job.
D) Repair the run with the new parameter, and update the task by adding the missing task parameter.

Solutions: