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Databricks Databricks-Certified-Data-Engineer-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Data Engineer Associate Exam
Exam Number:	DE-A
Exam Format:	Scenario-based questions, Multiple-choice, Proctored online or test center
Related Certifications:	Databricks Certified Data Engineer Professional
Available Languages:	English
Real Exam Qty:	45 scored multiple-choice questions
Certificate Validity Period:	2 years
Exam Price:	USD 200
Passing Score:	Approximately ~70% (not officially fixed/publicly guaranteed)
Exam Duration:	90 minutes
Recommended Training:	Databricks Data Engineering Learning Path (Databricks Academy) Official Data Engineering with Databricks Course
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions
Exam Way:	Online proctored exam or test center
Pre Condition:	No formal prerequisites required; recommended ~6 months hands-on experience with Databricks, Spark SQL, and PySpark.
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-associate

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q25-Q30):

NEW QUESTION # 25
A Databricks workflow fails at the last stage due to an error in a notebook. This workflow runs daily. The data engineer fixes the mistake and wants to rerun the pipeline. This workflow is very costly and time- intensive to run.
Which action should the data engineer do in order to minimise downtime and cost?

A. Switch to another cluster
B. Re-run the entire workflow
C. Repair run
D. Restart the cluster

Answer: C

NEW QUESTION # 26
A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped.
Which of the following approaches can the data engineer take to identify the table that is dropping the records?

A. They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.
B. They cannot determine which table is dropping the records.
C. They can set up separate expectations for each table when developing their DLT pipeline.
D. They can set up DLT to notify them via email when records are dropped.
E. They can navigate to the DLT pipeline page, click on the "Error" button, and review the present errors.

Answer: A

Explanation:
Explanation
To identify the table in a Delta Live Tables (DLT) pipeline where data is being dropped due to quality concerns, the data engineer can navigate to the DLT pipeline page, click on each table in the pipeline, and view the data quality statistics. These statistics often include information about records dropped, violations of expectations, and other data quality metrics. By examining the data quality statistics for each table in the pipeline, the data engineer can determine at which table the data is being dropped.

NEW QUESTION # 27
A data engineer at a company that uses Databricks with Unity Catalog needs to share a collection of tables with an external partner who also uses a Databricks workspace enabled for Unity Catalog. The data engineer decides to use Delta Sharing to accomplish this.
What is the first piece of information the data engineer should request from the external partner to set up Delta Sharing?

A. The sharing identifier of their Unity Catalog metastore
B. The name of their Databricks cluster
C. Their Databricks account password
D. The IP address of their Databricks workspace

Answer: A

NEW QUESTION # 28
A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities.
Which type of Databricks cluster should the data engineer configure?

A. A high-concurrency cluster designed for interactive SQL workloads.
B. A job cluster configured to autoscale across multiple workers during the pipeline run.
C. A lightweight single-node cluster with low worker node count to reduce costs.
D. An all-purpose cluster always kept running to ensure low-latency job startup times.

Answer: B

Explanation:
Job clusters are optimized for automated production workloads. They start when a job is triggered and terminate automatically once the task completes. This ensures cost control while maintaining performance and reliability for batch ETL. Autoscaling allows Databricks to add or remove workers dynamically based on workload size, ensuring large data volumes are processed efficiently.
All-purpose clusters are intended for development or ad-hoc workloads, not scheduled ETL.
Reference Source: Databricks Compute and Job Cluster Configuration Documentation - "Autoscaling and Job Clusters."

NEW QUESTION # 29
A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped.
Which of the following approaches can the data engineer take to identify the table that is dropping the records?

A. They can navigate to the DLT pipeline page, click on the "Error" button, and review the present errors.
B. They cannot determine which table is dropping the records.
C. They can set up separate expectations for each table when developing their DLT pipeline.
D. They can set up DLT to notify them via email when records are dropped.
E. They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.

Answer: A

NEW QUESTION # 30
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