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Databricks Certified Professional Data Engineer exam is designed to test a candidate's knowledge and skills in building, designing, and managing data pipelines on the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam covers a range of topics, including data processing, data storage, data warehousing, data modeling, and data architecture. Candidates are expected to have a deep understanding of these topics and be able to apply them in real-world scenarios.

Databricks Certified Professional Data Engineer is an exam designed for professionals who are willing to demonstrate their expertise in building and managing big data pipelines using Databricks. Databricks is a unified analytics platform that provides a collaborative environment for processing large-scale data. The Databricks Certified Professional Data Engineer exam validates the candidate's ability to design, build, and deploy large-scale data processing solutions using Databricks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q213-Q218):

NEW QUESTION # 213
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame namedpredswith the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A. preds.write.mode("append").saveAsTable("churn_preds")
B.
C.
D.
E. preds.write.format("delta").save("/preds/churn_preds")

Answer: A

NEW QUESTION # 214
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.
address THEN
UPDATE SET current = false, end_date = staged_updates.effective_date
WHEN NOT MATCHED THEN
INSERT (customer_id, address, current, effective_date, end_date)
VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?
* The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.

A. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
B. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
C. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

Answer: B

Explanation:
The provided MERGE statement is a classic implementation of a Type 2 SCD in a data warehousing context.
In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in the customers table is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.
Databricks documentation on implementing SCDs using Delta Lake and the MERGE statement (https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge).

NEW QUESTION # 215
The Databricks CLI is used to trigger a run of an existing job by passing the job_id parameter. The response indicating the job run request was submitted successfully includes a field run_id. Which statement describes what the number alongside this field represents?

A. The job_id is returned in this field.
B. The number of times the job definition has been run in this workspace.
C. The globally unique ID of the newly triggered run.
D. The job_id and number of times the job has been run are concatenated and returned.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "run_id: The canonical identifier of a run."
Exact extract: "Each job run has a unique run_id."

NEW QUESTION # 216
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 persist after the pipeline is stopped to allow for additional testing
B. 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
C. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated
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 continuously and the pipeline will not shut down. The compute resources will persist with the pipeline (Correct)

Answer: E

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 # 217
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 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.
B. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
C. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
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 User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.

Answer: A

NEW QUESTION # 218
......

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