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Databricks Certified Professional Data Engineer Exam Sample Questions (Q67-Q72):

NEW QUESTION # 67
Which is a key benefit of an end-to-end test?

A. It provides testing coverage for all code paths and branches.
B. It pinpoint errors in the building blocks of your application.
C. It makes it easier to automate your test suite
D. It closely simulates real world usage of your application.

Answer: D

Explanation:
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real-world, user behavior, ensuring that the system as a whole operates correctly.
References:
* Software Testing: End-to-End Testing

NEW QUESTION # 68
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion. However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

A. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
B. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
C. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
D. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
E. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.

Answer: D

Explanation:
Partitioning the data by the topic field allows the company to apply different access control policies and retention policies for different topics. For example, the company can use the Table Access Control feature to grant or revoke permissions to the registration topic based on user roles or groups. The company can also use the DELETE command to remove records from the registration topic that are older than 14 days, while keeping the records from other topics indefinitely. Partitioning by the topic field also improves the performance of queries that filter by the topic field, as they can skip reading irrelevant partitions. References:
* Table Access Control: https://docs.databricks.com/security/access-control/table-acls/index.html
* DELETE: https://docs.databricks.com/delta/delta-update.html#delete-from-a-table

NEW QUESTION # 69
Data engineering team has a job currently setup to run a task load data into a reporting table every day at 8: 00 AM takes about 20 mins, Operations teams are planning to use that data to run a second job, so they access latest complete set of data. What is the best to way to orchestrate this job setup?

A. Add Operation reporting task in the same job and set the operations reporting task to depend on Data Engineering task
B. Setup a second job to run at 8:20 AM in the same workspace
C. Setup a Delta live to table based on the first table, set the job to run in continuous mode
D. Add Operation reporting task in the same job and set the Data Engineering task to de-pend on Operations reporting task
E. Use Auto Loader to run every 20 mins to read the initial table and set the trigger to once and create a second job

Answer: A

Explanation:
Explanation
The answer is Add Operation reporting task in the same job and set the operations reporting task to depend on Data Engineering task.

Diagram Description automatically generated with medium confidence

NEW QUESTION # 70
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 once and the pipeline will shut down. The compute resources will be terminated
B. 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
C. 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
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 # 71
The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalidlatitudeandlongitudevalues in theactivity_detailstable have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to addCHECKconstraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

A. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
B. The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
C. The activity details table already exists; CHECK constraints can only be added during initial table creation.
D. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.
E. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.

Answer: D

Explanation:
The failure is that the code to add CHECK constraints to the Delta Lake table fails when executed. The code uses ALTER TABLE ADD CONSTRAINT commands to add two CHECK constraints to a table named activity_details. The first constraint checks if the latitude value is between -90 and 90, and the second constraint checks if the longitude value is between -180 and 180. The cause of this failure is that the activity_details table already contains records that violate these constraints, meaning that they have invalid latitude or longitude values outside of these ranges. When adding CHECK constraints to an existing table, Delta Lake verifies that all existing data satisfies the constraints before adding them to the table. If any record violates the constraints, Delta Lake throws an exception and aborts the operation. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Add a CHECK constraint to an existing table" section.
https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-alter-table.html#add-constraint

NEW QUESTION # 72
......

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