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Reliable Databricks-Certified-Data-Engineer-Associate Test Notes, New Databricks-Certified-Data-Engineer-Associate Braindumps Questions

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
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 PRIVILEGES ON DATABASE customers TO teams;
B. GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;
C. GRANT USAGE ON DATABASE customers TO team;
D. GRANT ALL PRIVILEGES ON DATABASE team TO customers;
E. GRANT ALL PRIVILEGES ON DATABASE customers TO team;

Answer: E

Explanation:
Explanation
To grant full privileges on the database "customers" to the new data engineering team, you can use the GRANT ALL PRIVILEGES command as shown in option E. This command provides the team with all possible privileges on the specified database, allowing them to fully manage it.

NEW QUESTION # 20
Which of the following describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?

A. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally.
B. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.
C. CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static.
D. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.
E. CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.

Answer: A

Explanation:
A streaming live table or view processes data that has been added only since the last pipeline update.
Streaming tables and views are stateful; if the defining query changes, new data will be processed based on the new query and existing data is not recomputed. This is useful when data needs to be processed incrementally, such as when ingesting streaming data sources or performing incremental loads from batch data sources. A live table or view, on the other hand, may be entirely computed when possible to optimize computation resources and time. This is suitable when data needs to be processed in full, such as when performing complex transformations or aggregations that require scanning all the data. References: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.

NEW QUESTION # 21
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 VACUUM command was run on the table
B. The OPTIMIZE command was nun on the table
C. The DELETE HISTORY command was run on the table
D. The HISTORY command was run on the table
E. The TIME TRAVEL command was run on the table

Answer: A

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 # 22
Which of the following describes the storage organization of a Delta table?

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

Answer: E

Explanation:
Explanation
Delta tables store data in a structured manner using Parquet files, and they also maintain metadata and transaction logs in separate directories. This organization allows for versioning, transactional capabilities, and metadata tracking in Delta Lake. Thank you for pointing out the error, and I appreciate your understanding.

NEW QUESTION # 23
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?

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

Answer: A

Explanation:
The spark.sql operation allows the data engineering team to run a SQL query and return the result as a PySpark DataFrame. This way, the data engineering team can use the same query that the data analyst has developed and operate with the results in PySpark. For example, the data engineering team can use spark.sql("SELECT * FROM sales") to get a DataFrame of all the records from the sales Delta table, and then apply various tests or transformations using PySpark APIs. The other options are either not valid operations (A, D), not suitable for running a SQL query (B, E), or not returning a DataFrame (A). Reference: Databricks Documentation - Run SQL queries, Databricks Documentation - Spark SQL and DataFrames.

NEW QUESTION # 24
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