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Databricks-Certified-Data-Engineer-Associate certification is highly regarded in the industry and is recognized by many organizations as a mark of excellence in data engineering. Databricks Certified Data Engineer Associate Exam certification can help data engineers to advance their careers and increase their earning potential. It can also help employers to identify skilled and knowledgeable data engineers who can help them to unlock the value of their data.

One of the unique features of the Databricks Certified Data Engineer Associate certification is that it is focused on Databricks, a cloud-based big data processing platform. Databricks is becoming increasingly popular among data professionals, and the certification is designed to test individuals' knowledge of this platform. This makes the certification especially valuable for individuals who work with Databricks or plan to work with it in the future.

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is designed for professionals who are interested in validating their expertise in building and managing big data processing systems using Databricks. Databricks is a unified data analytics platform that offers a cloud-based environment for processing big data workloads. Databricks-Certified-Data-Engineer-Associate Exam covers a wide range of topics, including data engineering, data processing, data storage, and data analysis.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q45-Q50):

NEW QUESTION # 45
A data engineering team is using Kafka to capture event data and then ingest it into Databricks. The team wants to be able to see these historical events. Medallion architecture is already in place. The team wants to be mindful of costs.
Where should this historical event data be stored?

A. Raw layer
B. Bronze
C. Gold
D. Silver

Answer: B

NEW QUESTION # 46
A data engineer wants to update specific rows in a Delta table based on matching keys from another dataset. The operation must support both insert and update logic in a single statement.
Which Delta Lake command supports this functionality?

A. UPDATE
B. DELETE
C. MERGE INTO
D. INSERT INTO

Answer: C

NEW QUESTION # 47
Which compute option should be chosen in a scenario where small-scale ad hoc Python scripts need to be run at high frequency and should wind down quickly after these queries have finished running?

A. SQL Warehouse
B. Serverless compute
C. All-purpose cluster
D. Job cluster

Answer: B

Explanation:
For small-scale, ad hoc Python workloads that run frequently and should start quickly and stop without leaving idle infrastructure running, serverless compute is the best fit. Databricks documentation explains that serverless compute allows users to run workloads without provisioning compute resources manually , and Databricks automatically allocates and manages the required resources. This improves startup and scaling speed and minimizes idle time, which directly matches the requirement that compute should "wind down quickly" after the work completes. Databricks also recommends serverless compute broadly when the workload is supported because it is the simplest and most reliable option. An all-purpose cluster is better suited to interactive collaborative development and can remain running longer than needed. A job cluster is ephemeral and often a good batch choice, but it still requires more explicit configuration than serverless and is not as optimized for minimal operational overhead. A SQL warehouse is intended for SQL workloads, not general ad hoc Python script execution. Therefore, serverless compute is the most appropriate choice.

NEW QUESTION # 48
A data engineer triggers a scheduled job but finds that the new run was not executed. The run history shows the run was skipped with a concurrency-related queue message. Which configuration should the engineer investigate?

A. The cluster spot-instance availability in the cloud provider
B. The task-level timeout_seconds configuration
C. The job-level max_concurrent_runs setting and whether queueing is enabled
D. The SQL warehouse permissions for the task

Answer: C

Explanation:
A run is skipped when the job has reached its max_concurrent_runs limit and queueing is disabled. The engineer should check both job-level settings; enabling queueing allows the run to wait for capacity instead of being skipped.

NEW QUESTION # 49
A data engineer needs to incrementally load thousands of new JSON files that land in a cloud storage location every hour. The solution must track which files have already been processed without listing the entire directory each run, and must scale to millions of files over time.
Which approach should the engineer use?

A. Use spark.read.json() on the full directory and overwrite the target table each hour.
B. Use Auto Loader with cloudFiles.format set to json and a checkpoint location for file state tracking.
C. Use COPY INTO with FORCE = true to reprocess all files each run.
D. Use a nightly MERGE INTO from a manually maintained list of file paths.

Answer: B

Explanation:
Auto Loader (cloudFiles) incrementally processes new files as they arrive and maintains processed-file state in RocksDB under the checkpoint location, so it does not need to re-list the source directory. It scales to billions of files and supports file notification mode for very large directories.

NEW QUESTION # 50
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