Quiz 2026 Databricks-Certified-Professional-Data-Engineer: Databricks Certified Professional Data Engineer Exam–Reliable Latest Test Labs

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

Section	Weight	Objectives
Data Ingestion	15-20%	- Batch ingestion methods 1. Spark APIs for ingestion 2. Integration with external systems 3. DBR autoloader - Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Jobs and job scheduling 2. Monitoring and alerting 3. Task dependencies and orchestration
Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. DataFrame operations and transformations 2. Window functions 3. Spark SQL queries and functions - Python and SQL for data engineering 1. Spark APIs in Python 2. Performance optimization techniques 3. Built-in and user-defined functions
Delta Lake	20-25%	- Delta Lake fundamentals 1. Optimize and Z-order 2. ACID transactions 3. Time travel and data versioning - Delta Lake operations 1. Merge, update, delete operations 2. Delta Live Tables 3. Schema evolution and enforcement
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Bronze, silver, gold data layers 2. Data governance fundamentals 3. Differences between data lake, data warehouse, and lakehouse

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q128-Q133):

NEW QUESTION # 128
How VACCUM and OPTIMIZE commands can be used to manage the DELTA lake?

A. VACCUM command can be used to compact small parquet files, and the OP-TIMZE command can be used to delete parquet files that are marked for dele-tion/unused.
B. VACCUM command can be used to delete empty/blank parquet files in a delta table. OPTIMIZE command can be used to update stale statistics on a delta table.
C. VACCUM command can be used to compress the parquet files to reduce the size of the table, OPTIMIZE command can be used to cache frequently delta tables for better performance.
D. OPTIMIZE command can be used to compact small parquet files, and the VAC-CUM command can be used to delete parquet files that are marked for deletion/unused.
(Correct)
E. VACCUM command can be used to delete empty/blank parquet files in a delta table, OPTIMIZE command can be used to cache frequently delta tables for better perfor-mance.

Answer: D

Explanation:
Explanation
VACCUM:
You can remove files no longer referenced by a Delta table and are older than the retention thresh-old by running the vacuum command on the table. vacuum is not triggered automatically. The de-fault retention threshold for the files is 7 days. To change this behavior, see Configure data reten-tion for time travel.
OPTIMIZE:
Using OPTIMIZE you can compact data files on Delta Lake, this can improve the speed of read queries on the table. Too many small files can significantly degrade the performance of the query.

NEW QUESTION # 129
The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
B. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
C. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
D. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
E. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.

Answer: B

Explanation:
The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion. The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delete from a table" section; Databricks Documentation, under "Remove files no longer referenced by a Delta table" section.

NEW QUESTION # 130
You are currently working on a notebook that will populate a reporting table for downstream process consumption, this process needs to run on a schedule every hour, what type of cluster are you going to use to set up this job?

A. Use Azure VM to read and write delta tables in Python
B. Since it's just a single job and we need to run every hour, we can use an all-purpose cluster
C. Use delta live table pipeline to run in continuous mode
D. The job cluster is best suited for this purpose.

Answer: D

Explanation:
Explanation
The answer is, The Job cluster is best suited for this purpose.
Since you don't need to interact with the notebook during the execution especially when it's a scheduled job, job cluster makes sense. Using an all-purpose cluster can be twice as expensive as a job cluster.
FYI,
When you run a job scheduler with option of creating a new cluster when the job is complete it terminates the cluster. You cannot restart a job cluster.

NEW QUESTION # 131
You are currently working on a project that requires the use of SQL and Python in a given note-book, what would be your approach

A. A single notebook can support multiple languages, use the magic command to switch between the two.
B. Use an All-purpose cluster for python, SQL endpoint for SQL
C. Create two separate notebooks, one for SQL and the second for Python
D. Use job cluster to run python and SQL Endpoint for SQL

Answer: A

Explanation:
Explanation
The answer is, A single notebook can support multiple languages, use the magic command to switch between the two.
Use %sql and %python magic commands within the same notebook

NEW QUESTION # 132
The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
B. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
C. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
D. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
E. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.

Answer: B

Explanation:
The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion.
The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage.
Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delete from a table" section; Databricks Documentation, under "Remove files no longer referenced by a Delta table" section.

NEW QUESTION # 133
......

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