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To pass the Databricks-Certified-Data-Engineer-Associate Exam, candidates must demonstrate their ability to work with the Databricks platform to design, build, and manage data pipelines. They must also be able to analyze data and provide insights to stakeholders using advanced analytics tools and techniques. This requires a deep understanding of data structures, algorithms, and programming languages like Python and SQL.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, 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 persist to allow for additional testing.
B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
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 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.
E. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.

Answer: D

Explanation:
Explanation
In a Delta Live Table pipeline running in Continuous Pipeline Mode, when you click Start to update the pipeline, the following outcome is expected: All datasets defined using STREAMING LIVE TABLE and LIVE TABLE against Delta Lake table sources will be updated at set intervals. The compute resources will be deployed for the update process and will be active during the execution of the pipeline. The compute resources will be terminated when the pipeline is stopped or shut down. This mode allows for continuous and periodic updates to the datasets as new data arrives or changes in the underlying Delta Lake tables occur. The compute resources are provisioned and utilized during the update intervals to process the data and perform the necessary operations.

NEW QUESTION # 57
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?

A. The ability to manipulate the same data using a variety of languages
B. The ability to set up alerts for query failures
C. The ability to support batch and streaming workloads
D. The ability to collaborate in real time on a single notebook
E. The ability to distribute complex data operations

Answer: C

Explanation:
Explanation
Delta Lake is a key component of the Databricks Lakehouse Platform that provides several benefits, and one of the most significant benefits is its ability to support both batch and streaming workloads seamlessly. Delta Lake allows you to process and analyze data in real-time (streaming) as well as in batch, making it a versatile choice for various data processing needs. While the other options may be benefits or capabilities of Databricks or the Lakehouse Platform in general, they are not specifically associated with Delta Lake.

NEW QUESTION # 58
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?

A. They can turn on the Auto Stop feature for the SQL endpoint.
B. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized."
C. They can turn on the Serverless feature for the SQL endpoint.
D. They can increase the maximum bound of the SQL endpoint's scaling range
E. They can increase the cluster size of the SQL endpoint.

Answer: E

Explanation:
Explanation
https://www.databricks.com/blog/2022/03/10/top-5-databricks-performance-tips.html

NEW QUESTION # 59
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?

A. spark.sql("sales")
B. SELECT * FROM sales
C. spark.table("sales")
D. There is no way to share data between PySpark and SQL.
E. spark.delta.table("sales")

Answer: C

Explanation:
Explanation
https://spark.apache.org/docs/3.2.1/api/python/reference/api/pyspark.sql.SparkSession.table.html

NEW QUESTION # 60
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

A. The pipeline will need to stop using the medallion-based multi-hop architecture
B. None of these changes will need to be made
C. The pipeline will need to be written entirely in SQL
D. The pipeline will need to be written entirely in Python
E. The pipeline will need to use a batch source in place of a streaming source

Answer: B

NEW QUESTION # 61
......

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