Databricks-Certified-Data-Engineer-Associate Practical Information, Databricks-Certified-Data-Engineer-Associate Dumps Guide

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Databricks Certified Data Engineer Associate exam is designed for data engineers who work with the Databricks platform and have experience in designing and implementing data pipelines. Databricks-Certified-Data-Engineer-Associate Exam covers a wide range of topics, including data ingestion, transformation, storage, and processing. It also covers advanced concepts such as Spark architecture, performance tuning, and optimization.

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The GAQM Databricks-Certified-Data-Engineer-Associate exam is a challenging and comprehensive exam that tests the individual's knowledge of Databricks and their ability to design and implement data-driven solutions. With the right preparation and experience, individuals can successfully pass the exam and earn the Databricks-Certified-Data-Engineer-Associate Certification.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q320-Q325):

NEW QUESTION # 320
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?

A. The OPTIMIZE command was nun on the table
B. The HISTORY command was run on the table
C. The VACUUM command was run on the table
D. The TIME TRAVEL command was run on the table
E. The DELETE HISTORY command was run on the table

Answer: C

Explanation:
The VACUUM command is used to remove files that are no longer referenced by a Delta table and are older than the retention threshold1. The default retention period is 7 days2, but it can be changed by setting the delta.logRetentionDuration and delta.deletedFileRetentionDuration configurations3. If the VACUUM command was run on the table with a retention period shorter than 3 days, then the data files that were needed to restore the table to a 3-day-old version would have been deleted. The other commands do not delete data files from the table. The TIME TRAVEL command is used to query a historical version of the table4. The DELETE HISTORY command is not a valid command in Delta Lake. The OPTIMIZE command is used to improve the performance of the table by compacting small files into larger ones5. The HISTORY command is used to retrieve information about the operations performed on the table. Reference: 1: VACUUM | Databricks on AWS 2: Work with Delta Lake table history | Databricks on AWS 3: [Delta Lake configuration | Databricks on AWS] 4: Work with Delta Lake table history - Azure Databricks 5: [OPTIMIZE | Databricks on AWS] : [HISTORY | Databricks on AWS]

NEW QUESTION # 321
A data engineer ingests semi-structured JSON logs into a Delta table using Auto Loader with schema evolution enabled. A new string field userAgent appears in the JSON. What happens to the new userAgent field?

A. Ingestion fails with a schema-mismatch error until the table is altered.
B. The new userAgent field is added as a new nullable column in the Delta table, populated for new data.
C. All existing data is backfilled with a default value for userAgent and made non-nullable.
D. The new userAgent field is silently ignored, and its data is dropped for all new files.

Answer: B

Explanation:
With schema evolution enabled, Auto Loader adds userAgent as a nullable column. Existing rows have NULL, while new records populate the field when present.

NEW QUESTION # 322
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
B. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
C. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
D. Records that violate the expectation cause the job to fail.

Answer: A

Explanation:
The expected behavior when a batch of data containing data that violates the expectation is processed is that the job will fail. This is because the expectation clause has the ON VIOLATION FAIL UPDATE option, which means that if any record in the batch does not meet the expectation, the entire batch will be rejected and the job will fail. This option is useful for enforcing strict data quality rules and preventing invalid data from entering the target dataset.
Option A is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION DROP RECORD option should be used.
Option C is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and load them into a quarantine table, the ON VIOLATION QUARANTINE RECORD option should be used.
Option D is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION LOG RECORD option should be used.
Option E is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and flag them as invalid in a field added to the target dataset, the ON VIOLATION FLAG RECORD option should be used.
Delta Live Tables Expectations
[Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 323
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

Answer: A

Explanation:
The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

NEW QUESTION # 324
Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?

A. Ability to scale storage
B. Ability to scale workloads
C. Cloud-specific integrations
D. Avoiding vendor lock-in
E. Simplified governance

Answer: D

Explanation:
One of the benefits of the Databricks Lakehouse Platform embracing open source technologies is that it avoids vendor lock-in. This means that customers can use the same open source tools and frameworks across different cloud providers, and migrate their data and workloads without being tied to a specific vendor. The Databricks Lakehouse Platform is built on open source projects such as Apache Spark™, Delta Lake, MLflow, and Redash, which are widely used and trusted by millions of developers. By supporting these open source technologies, the DatabricksLakehouse Platform enables customers to leverage the innovation and community of the open source ecosystem, and avoid the risk of being locked into proprietary or closed solutions. The other options are either not related to open source technologies (A, B, C, D), or not benefits of the Databricks Lakehouse Platform (A, B). References: Databricks Documentation - Built on open source, Databricks Documentation - What is the Lakehouse Platform?, Databricks Blog - Introducing the Databricks Lakehouse Platform.

NEW QUESTION # 325
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