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The Databricks Certified Data Engineer Associate Exam certification exam is designed to test the candidate's knowledge of Databricks and their ability to build and manage data pipelines using the platform. Databricks-Certified-Data-Engineer-Associate exam consists of multiple-choice questions and is timed, with a maximum time limit of 120 minutes. To pass the exam, candidates must achieve a minimum score of 70%. Databricks-Certified-Data-Engineer-Associate exam is available online and can be taken from anywhere in the world, making it accessible to data engineers from all backgrounds and locations.

The GAQM Databricks-Certified-Data-Engineer-Associate Exam consists of multiple-choice questions that cover various topics related to data engineering using Databricks. Databricks-Certified-Data-Engineer-Associate exam tests a candidate's knowledge of data engineering concepts, big data processing and analytics, and cloud computing using Databricks. Candidates are required to pass the exam to earn the Databricks Certified Data Engineer Associate certification.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q52-Q57):

NEW QUESTION # 52
An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query.
For the first week following the project's release, the managerwants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project's release.
Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project's release?

A. They can set the query's refresh schedule to end after a certain number of refreshes.
B. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint.
C. They cannot ensure the query does not cost the organization money beyond the first week of the project's release.
D. They can set a limit to the number of individuals that are able to manage the query's refresh schedule.
E. They can set the query's refresh schedule to end on a certain date in the query scheduler.

Answer: E

NEW QUESTION # 53
A data engineer has realized that the data files associated with a Delta table are incredibly small. They want to compact the small files to form larger files to improve performance.
Which of the following keywords can be used to compact the small files?

A. COMPACTION
B. OPTIMIZE
C. VACUUM
D. REDUCE
E. REPARTITION

Answer: B

Explanation:
The keyword that can be used to compact the small files associated with a Delta table is OPTIMIZE. The OPTIMIZE command performs file compaction on a Delta table by rewriting a set of small files into a set of larger files1. This can improve the performance of queries that scan the table by reducing the number of files that need to be read and the amount of metadata that needs to be processed1. The OPTIMIZE command can also optionally sort the data within each file by a given set of columns, which can further improve the query performance by enabling data skipping and predicate pushdown1. The OPTIMIZE command can be applied to the whole table or to a specific partition of the table1.
The other keywords are not suitable for compacting the small files associated with a Delta table. REDUCE is a keyword used in the SQL syntax for aggregating data using a user-defined function2. COMPACTION is not a valid keyword in SQL or Python. REPARTITION is a keyword used in the Python syntax for changing the number of partitions of a DataFrame or an RDD3. VACUUM is a keyword used to remove files that are no longer referenced by a Delta table and are older than a retention threshold4.
Reference:
1: OPTIMIZE | Databricks on AWS
2: REDUCE | Databricks on AWS
3: repartition | Databricks on AWS
4: VACUUM | Databricks on AWS

NEW QUESTION # 54
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

Answer: D

Explanation:
Explanation
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html

NEW QUESTION # 55
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team's queries?

A. They can turn on the Serverless 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 Auto Stop 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: D

NEW QUESTION # 56
Which of the following describes the storage organization of a Delta table?

A. Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes.
B. Delta tables are stored in a collection of files that contain only the data stored within the table.
C. Delta tables are stored in a single file that contains only the data stored within the table.
D. Delta tables are stored in a single file that contains data, history, metadata, and other attributes.
E. Delta tables store their data in a single file and all metadata in a collection of files in a separate location.

Answer: A

Explanation:
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling1. Delta Lake stores its data and metadata in a collection of files in a directory on a cloud storage system, such as AWS S3 or Azure Data Lake Storage2. Each Delta table has a transaction log that records the history of operations performed on the table, such as insert, update, delete, merge, etc. The transaction log also stores the schema and partitioning information of the table2. The transaction log enables Delta Lake to provide ACID guarantees, time travel, schema enforcement, and other features1. References:
* What is Delta Lake? | Databricks on AWS
* Quickstart - Delta Lake Documentation

NEW QUESTION # 57
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