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Databricks Certified Professional Data Engineer certification is designed for data engineers who are responsible for building and maintaining data pipelines and data lakes on the Databricks platform. Databricks Certified Professional Data Engineer Exam certification exam covers a wide range of topics, including data engineering concepts, data modeling, data ingestion, data transformation, data processing, and data warehousing. Databricks-Certified-Professional-Data-Engineer exam is designed to assess a candidate's ability to design, build, and maintain scalable and reliable data pipelines on the Databricks platform.

Databricks Certified Professional Data Engineer exam is a rigorous test that measures a candidate's knowledge and skills in various areas of Databricks, including data engineering, data modeling, data integration, data processing, and data storage. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and performance-based tasks that require candidates to demonstrate their ability to solve real-world problems using Databricks. Databricks-Certified-Professional-Data-Engineer exam is designed to assess the candidate's ability to design and implement scalable, reliable, and efficient data solutions that meet business requirements.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q97-Q102):

NEW QUESTION # 97
Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
B. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
C. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
D. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
E. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.

Answer: D

Explanation:
This is the correct answer because it characterizes the general programming model used by Spark Structured Streaming, which is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model, where users can express their streaming computation using the same Dataset/DataFrame API as they would use for static data. The Spark SQL engine will take care of running the streaming query incrementally and continuously and updating the final result as streaming data continues to arrive. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Overview" section.

NEW QUESTION # 98
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

A. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
B. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
C. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
D. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
E. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.

Answer: D

Explanation:
Explanation
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled.
Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; [Databricks Documentation], under "Jobs API - Create" section.

NEW QUESTION # 99
The Delta Live Table Pipeline is configured to run in Production mode using the continuous Pipe-line Mode.
what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped
B. All datasets will be updated continuously and the pipeline will not shut down. The compute resources will persist with the pipeline (Correct)
C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist after the pipeline is stopped to allow for additional testing
D. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing
E. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated

Answer: B

Explanation:
Explanation
The answer is,
All datasets will be updated continuously and the pipeline will not shut down. The compute re-sources will persist with the pipeline until it is shut down since the execution mode is chosen to be continuous. It does not matter if the pipeline mode is development or production, pipeline mode only matters during the pipeline initialization.
DLT pipeline supports two modes Development and Production, you can switch between the two based on the stage of your development and deployment lifecycle.
Development and production modes
Development:
When you run your pipeline in development mode, the Delta Live Tables system:
*Reuses a cluster to avoid the overhead of restarts.
*Disables pipeline retries so you can immediately detect and fix errors.
Production:
In production mode, the Delta Live Tables system:
*Restarts the cluster for specific recoverable errors, including memory leaks and stale cre-dentials.
*Retries execution in the event of specific errors, for example, a failure to start a cluster.
Use the buttons in the Pipelines UI to switch between develop-ment and production modes. By default,

pipelines run in development mode.
Switching between development and production modes only controls cluster and pipeline execution behavior.
Storage locations must be configured as part of pipeline settings and are not affected when switching between modes.
Delta Live Tables supports two different modes of execution:
Triggered pipelines update each table with whatever data is currently available and then stop the cluster running the pipeline. Delta Live Tables automatically analyzes the dependencies between your tables and starts by computing those that read from external sources. Tables within the pipe-line are updated after their dependent data sources have been updated.
Continuous pipelines update tables continuously as input data changes. Once an update is started, it continues to run until manually stopped. Continuous pipelines require an always-running cluster but ensure that downstream consumers have the most up-to-date data Please review additional DLT concepts using the below link
https://docs.databricks.com/data-engineering/delta-live-tables/delta-live-tables-concepts.html#delta-live-tables-c

NEW QUESTION # 100
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

A. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
B. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
C. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
D. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
E. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.

Answer: D

Explanation:
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled.
Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; [Databricks Documentation], under "Jobs API - Create" section.

NEW QUESTION # 101
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
B. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
C. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
D. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
E. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.

Answer: E

Explanation:
Explanation
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs. Verified References: [Databricks Certified Data Engineer Professional], under
"Databricks Workspace" section; Databricks Documentation, under "Workspace audit logs" section.

NEW QUESTION # 102
......

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Databricks Latest Databricks-Certified-Professional-Data-Engineer Test Prep: Databricks Certified Professional Data Engineer Exam - RealVCE Download Demo Free

Proven Way to Pass the Databricks Databricks-Certified-Professional-Data-Engineer Exam on the First Attempt

Databricks Certified Professional Data Engineer Exam Sample Questions (Q97-Q102):

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