Databricks-Certified-Data-Engineer-Associate Reliable Exam Syllabus - Databricks-Certified-Data-Engineer-Associate Reliable Test Syllabus

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a certification that is designed to test the skills and knowledge of data engineers who work with Databricks. Data engineers play a critical role in any organization that relies on data to make decisions. They are responsible for the design, construction, and maintenance of data pipelines, data models, and data warehouses.

Databricks Certified Data Engineer Associate certification is an essential certification for individuals who want to pursue a career in data engineering. Databricks Certified Data Engineer Associate Exam certification exam covers a range of topics and is designed to ensure that candidates have the necessary skills and knowledge to build and maintain large-scale data pipelines. By earning this certification, candidates can demonstrate their expertise in data engineering and increase their chances of getting hired by top companies in the data and AI industry.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q139-Q144):

NEW QUESTION # 139
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 HISTORY command was run on the table
B. The DELETE HISTORY command was run on the table
C. The VACUUM command was run on the table
D. The OPTIMIZE command was nun on the table
E. The TIME TRAVEL 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. References: 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 # 140
A new data engineering team team. has been assigned to an ELT project. The new data engineering team will need full privileges on the database customers to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?

A. GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;
B. GRANT ALL PRIVILEGES ON DATABASE team TO customers;
C. GRANT ALL PRIVILEGES ON DATABASE customers TO team;
D. GRANT USAGE ON DATABASE customers TO team;
E. GRANT SELECT PRIVILEGES ON DATABASE customers TO teams;

Answer: C

Explanation:
To grant full permissions on a database to a user, group, or service principal, the GRANT ALL PRIVILEGES ON DATABASE command can be used. This command grants all the applicable privileges on the database, such as CREATE, SELECT, MODIFY, and USAGE. The other options are either incorrect or incomplete, as they do not grant all the privileges or specify the wrong database or principal. References:
* GRANT
* Privileges

NEW QUESTION # 141
A data engineer is getting a partner organization up to speed with Databricks account. Both teams share some business use cases. The data engineer has to share some of your Unity-Catalog managed delta tables and the notebook jobs creating those tables with the partner organization.
How can the data engineer seamlessly share the required information?

A. Share required datasets and notebooks via Delta Sharing. Manage permissions via Unity Catalog.
B. Zip all the code and share via email and allow data ingestion from your data lake
C. Share access to codebase via Github and allow them to ingest datasets from your Datalake.
D. Data and Notebooks can be shared simply using Unity Catalog.

Answer: A

NEW QUESTION # 142
A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.
Which of the following describes why Auto Loader inferred all of the columns to be of the string type?

A. Auto Loader cannot infer the schema of ingested data
B. All of the fields had at least one null value
C. Auto Loader only works with string data
D. There was a type mismatch between the specific schema and the inferred schema
E. JSON data is a text-based format

Answer: E

Explanation:
JSON data is a text-based format that represents data as a collection of name-value pairs. By default, when Auto Loader infers the schema of JSON data, it treats all columns as strings. This is because JSON data can have varying data types for the same column across different files or records, and Auto Loader does not attempt to reconcile these differences. For example, a column named "age" may have integer values in some files, but string values in others. To avoid data loss or errors, Auto Loader infers the column as a string type.
However, Auto Loader also provides an option to infer more precise column types based on the sample data.
This option is called cloudFiles.inferColumnTypes and it can be set to true or false. When set to true, Auto Loader tries to infer the exact data types of the columns, such as integers, floats, booleans, or nested structures.
When set to false, Auto Loader infers all columns as strings. The default value of this option is false. References: Configure schema inference and evolution in Auto Loader, Schema inference with auto loader (non-DLT and DLT), Using and Abusing Auto Loader's Inferred Schema, Explicit path to data or a defined schema required for Auto loader.

NEW QUESTION # 143
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 HISTORY command was run on the table
B. The DELETE HISTORY command was run on the table
C. The VACUUM command was run on the table
D. The OPTIMIZE command was nun on the table
E. The TIME TRAVEL command was run on the table

Answer: C

Explanation:
Explanation
The VACUUM command in Delta Lake is used to clean up and remove unnecessary data files that are no longer needed for time travel or query purposes. When you run VACUUMwith certain retention settings, it can delete older data files, which might include versions of data that are older than the specified retention period. If the data engineer is unable to restore the table to a version that is 3 days old because the data files have been deleted, it's likely because the VACUUM command was run on the table, removing the older data files as part of data cleanup.

NEW QUESTION # 144
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