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Databricks Certified Data Engineer Professional Exam Sample Questions (Q115-Q120):

NEW QUESTION # 115
Which statement describes a key benefit of an end-to-end test?

A. It pinpoint errors in the building blocks of your application.
B. It closely simulates real world usage of your application.
C. It makes it easier to automate your test suite
D. It provides testing coverage for all code paths and branches.

Answer: B

Explanation:
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real- world, user behavior, ensuring that the system as a whole operates correctly.
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NEW QUESTION # 116
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A. Task queueing resulting from improper thread pool assignment.
B. Network latency due to some cluster nodes being in different regions from the source data
C. Skew caused by more data being assigned to a subset of spark-partitions.
D. Spill resulting from attached volume storage being too small.
E. Credential validation errors while pulling data from an external system.

Answer: C

Explanation:
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills.

NEW QUESTION # 117
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device. Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot. How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

A. Set the pipelines, reset, allowed property to false on raw_iot
B. Set the pipelines, reset, allowed property to false on bpm_stats
C. Set the skipChangeCommits flag to true on bpm_stats
D. Set the SkipChangeCommits flag to true raw_lot

Answer: A

Explanation:
In Databricks Lakehouse, to retain manually deleted or updated records in the raw_iot table while recomputing downstream tables when a pipeline update is run, the property pipelines.reset.allowed should be set to false. This property prevents the system from resetting the state of the table, which includes the removal of the history of changes, during a pipeline update. By keeping this property as false, any changes to the raw_iot table, including manual deletes or updates, are retained, and recomputation of downstream tables, such as bpm_stats, can occur with the full history of data changes intact.

NEW QUESTION # 118
A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?
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A. Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
B. Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
C. Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
D. Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
E. Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.

Answer: A

Explanation:
https://www.databricks.com/blog/2017/05/22/running-streaming-jobs-day-10x-cost-savings.html

NEW QUESTION # 119
An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

A. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
B. An managed table will be created in the storage container mounted to /mnt/finance_eda_bucket.
C. A managed table will be created in the DBFS root storage container.
D. An external table will be created in the storage container mounted to /mnt/finance eda bucket.
E. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.

Answer: B

Explanation:
https://docs.databricks.com/en/data-governance/unity-catalog/create-schemas.html#language- SQL

NEW QUESTION # 120
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2025 Vce Databricks-Certified-Data-Engineer-Professional File - Databricks Databricks Certified Data Engineer Professional Exam - High-quality Databricks-Certified-Data-Engineer-Professional Accurate Prep Material

New Release Databricks-Certified-Data-Engineer-Professional PDF Questions [2025] - Databricks Databricks-Certified-Data-Engineer-Professional Exam Dumps

Databricks Certified Data Engineer Professional Exam Sample Questions (Q115-Q120):

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