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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) certification exam is an excellent opportunity for data professionals to validate their skills in designing, building, and maintaining data pipelines using Databricks. Databricks Certified Data Engineer Associate Exam certification exam covers a wide range of topics and is designed to test the candidate's knowledge of data engineering fundamentals, Databricks architecture, data modeling, and data processing. Passing the certification exam provides an individual with a globally recognized certification that can enhance their career prospects in the data engineering field.

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Databricks Certified Data Engineer Associate certification is suitable for data engineers, data scientists, and other professionals who work with big data and want to demonstrate their expertise in Databricks. It is also relevant for organizations that use Databricks to process and analyze large amounts of data, as it ensures that their employees have the necessary skills and knowledge to work with the platform effectively.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q204-Q209):

NEW QUESTION # 204
A data engineering team runs multiple daily batch jobs. The team needs to run the jobs on a fixed overnight schedule with consistent performance and controlled resource usage. The team wants to minimize operational overhead and reduce idle cluster costs.
Which compute option should the team use?

A. Classic jobs compute
B. Serverless jobs compute
C. SQL warehouses
D. Classic all-purpose compute

Answer: B

NEW QUESTION # 205
A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?

A. They can clone the existing task to a new Job and then edit it to run the new notebook.
B. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
C. They can create a new task in the existing Job and then add it as a dependency of the original task.
D. They can clone the existing task in the existing Job and update it to run the new notebook.
E. They can create a new job from scratch and add both tasks to run concurrently.

Answer: C

Explanation:
To set up the new task to run a new notebook prior to the original task in a single-task Job, the data engineer can use the following approach: In the existing Job, create a new task that corresponds to the new notebook that needs to be run. Set up the new task with the appropriate configuration, specifying the notebook to be executed and any necessary parameters or dependencies. Once the new task is created, designate it as a dependency of the original task in the Job configuration. This ensures that the new task is executed before the original task.

NEW QUESTION # 206
Which of the following is hosted completely in the control plane of the classic Databricks architecture?

A. Worker node
B. Databricks Filesystem
C. JDBC data source
D. Driver node
E. Databricks web application

Answer: E

Explanation:
The Databricks web application is the user interface that allows you to create and manage workspaces, clusters, notebooks, jobs, and other resources. It is hosted completely in the control plane of the classic Databricks architecture, which includes the backend services that Databricks manages in your Databricks account. The other options are part of the compute plane, which is where your data is processed by compute resources such as clusters. The compute plane is in your own cloud account and network. References: Databricks architecture overview, Security and Trust Center

NEW QUESTION # 207
A data engineering team runs nightly batch ETL pipelines that transform large volumes of data stored in Delta Lake. The workloads are predictable and run on a fixed schedule. The team wants automatic scaling and cost efficiency, paying only for compute used during job execution. Which Databricks compute option is appropriate for this workload?

A. A single-node compute cluster configured without autoscaling.
B. An all-purpose compute cluster configured with autoscaling.
C. A Databricks SQL warehouse configured for serverless execution.
D. A job compute cluster triggered by a scheduled workflow.

Answer: D

Explanation:
Job compute is designed for scheduled, automated workloads. It can autoscale for the ETL workload and terminates after the job finishes, avoiding the idle cost of persistent all-purpose compute.

NEW QUESTION # 208
A data engineer has finished developing a new Declarative Automation Bundle, formerly known as a Databricks Asset Bundle, for a streaming pipeline. The engineer must verify that the configuration is syntactically correct, deploy the code to the workspace, and finally trigger the pipeline to confirm that it runs as expected.
Which sequence of Databricks CLI commands performs these steps in the correct order?

A. databricks bundle validate → databricks bundle deploy → databricks bundle run
B. databricks bundle check → databricks bundle upload → databricks bundle start
C. databricks bundle validate → databricks bundle sync → databricks bundle execute
D. databricks bundle init → databricks bundle deploy → databricks bundle run

Answer: A

NEW QUESTION # 209
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