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Databricks Databricks-Certified-Professional-Data-Engineer Exam With Confidence Using Practice Dumps

Exam Code:
Databricks-Certified-Professional-Data-Engineer
Exam Name:
Databricks Certified Data Engineer Professional Exam
Certification:
Vendor:
Questions:
120
Last Updated:
Apr 26, 2025
Exam Status:
Stable
Databricks Databricks-Certified-Professional-Data-Engineer

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Databricks Certified Data Engineer Professional Exam Questions and Answers

Question 1

In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone.

A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.

Why are the cloned tables no longer working?

Options:

A.

The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.

B.

Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.

C.

The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command

D.

Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.

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Question 2

A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.

Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

Options:

A.

Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.

B.

Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.

C.

The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.

D.

Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.

E.

Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.

Question 3

A Delta Lake table representing metadata about content posts from users has the following schema:

user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE

This table is partitioned by the date column. A query is run with the following filter:

longitude < 20 & longitude > -20

Which statement describes how data will be filtered?

Options:

A.

Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.

B.

No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.

C.

The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.

D.

Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.

E.

The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.