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

Section	Weight	Objectives
Topic 1: Databricks Lakehouse Platform	24%	- Delta Lake - Unity Catalog - Data Management - Lakehouse Architecture
Topic 2: Data Modeling and Storage	20%	- Data Modeling - Storage Optimization - File Formats
Topic 3: Data Processing	28%	- Spark SQL - ETL Pipelines - Structured Streaming - Data Transformation
Topic 4: Monitoring and Troubleshooting	16%	- Troubleshooting - Performance Optimization - Monitoring
Topic 5: Data Quality and Governance	12%	- Data Lineage - Data Quality - Governance

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q51-Q56):

NEW QUESTION # 51
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
B. Whenever a table is being created, make sure that the location keyword is used.
C. When tables are created, make sure that the external keyword is used in the create table statement.
D. Whenever a database is being created, make sure that the location keyword is used Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
E. When the workspace is being configured, make sure that external cloud object storage has been mounted.

Answer: B

Explanation:
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it.

NEW QUESTION # 52
A data engineer is building a customer data pipeline in Lakeflow Spark Declarative Pipelines. The source is a cloud-based event stream with limited retention containing inserts, updates, and deletes for customer records. These changes are being applied using the AUTO CDC INTO syntax to maintain an SCD Type 1 table as the target table, customer_dim. How should the data engineer build a downstream job that streams from the customer_dim table to only act on updates and delete events, processing data incrementally?

A. Read change data feed from customer_dim table and apply filters to incrementally act on the change events.
B. Streaming from customer_dim table would only be possible in the case of SCD 2 retention.
C. When stored as SCD 1, the target of AUTO CDC INTO includes updates and deletes. Streaming from customer_dim can fail due to these operations. Instead, build another stream from the original source.
D. Use ignoreChanges flag while streaming from customer_dim to avoid breaking the pipeline during updates and deletes.

Answer: A

Explanation:
Reading the change data feed from the customer_dim table enables downstream processing to react specifically to update and delete events while operating incrementally. Change data feed exposes row-level change types and versions, making it the correct mechanism for streaming only the relevant changes from an SCD Type 1 table maintained with AUTO CDC INTO.

NEW QUESTION # 53
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 job cluster configured to autoscale across multiple workers during the pipeline run.
B. An all-purpose cluster always kept running to ensure low-latency job startup times.
C. A high-concurrency cluster designed for interactive SQL workloads.
D. A lightweight single-node cluster with low worker node count to reduce costs.

Answer: A

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.

NEW QUESTION # 54
A data engineer deploys a multi-task Databricks job that orchestrates three notebooks. One task intermittently fails with Exit Code 1 but succeeds on retry. The engineer needs to collect detailed logs for the failing attempts, including stdout/stderr and cluster lifecycle context, and share them with the platform team. What steps the data engineer needs to follow using built-in tools?

A. From the job run details page, export the job's logs or configure log delivery; then retrieve the compute driver logs and event logs from the compute details page to correlate stdout/stderr with cluster events.
B. Download worker logs directly from the Spark UI and ignore driver logs, as worker logs contain stdout/stderr for all tasks and cluster events.
C. Use the notebook interactive debugger to re-run the entire multi-task job, and capture step- through traces for the failing task.
D. Export the notebook run results to HTML; this bundle includes complete stdout, stderr, and cluster event history across all tasks.

Answer: A

Explanation:
The recommended way to troubleshoot and collect detailed job logs is through the Job Run Details page in Databricks. From there, engineers can export run logs or configure automatic log delivery to a storage destination. The driver and event logs available under compute details provide stdout, stderr, and cluster lifecycle context required for root-cause analysis.

NEW QUESTION # 55
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A. - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
B. - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
C. - Total VMs: 16
- 25 GB per Executor
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
- 10 Cores / Executor
D. - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor
E. - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor

Answer: C

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

NEW QUESTION # 56
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