Latest Databricks Databricks-Certified-Data-Engineer-Associate Exam Vce, Databricks-Certified-Data-Engineer-Associate Free Practice Exams

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Databricks Certified Data Engineer Associate certification is a valuable credential for data engineers who want to demonstrate their expertise in working with Databricks. Databricks Certified Data Engineer Associate Exam certification is recognized by leading organizations and can help data engineers to advance their careers. Databricks Certified Data Engineer Associate Exam certification is also a great way for data engineers to demonstrate their commitment to ongoing professional development and to stay up to date with the latest trends and technologies in the field.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q91-Q96):

NEW QUESTION # 91
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

Answer: C

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

NEW QUESTION # 92
A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail transactions in the month of April. There are no duplicate records between the tables.
Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?

A. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INTERSECT SELECT * from april_transactions;
B. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
OUTER JOIN SELECT * FROM april_transactions;
C. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION SELECT * FROM april_transactions;
D. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
MERGE SELECT * FROM april_transactions;
E. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INNER JOIN SELECT * FROM april_transactions;

Answer: C

NEW QUESTION # 93
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when it is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can reduce the cluster size of the SQL endpoint.
B. They can set up the dashboard's SQL endpoint to be serverless.
C. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
D. They can turn on the Auto Stop feature for the SQL endpoint.
E. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.

Answer: D

NEW QUESTION # 94
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 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.
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 persist without any processing. The compute resources will persist but go unused.
D. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
E. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.

Answer: A

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 # 95
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 the subsequent step in the DLT pipeline is static.
B. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.
C. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally.
D. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.
E. CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.

Answer: C

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 # 96
......

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LATEST DATABRICKS DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE EXAM VCE, DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE FREE PRACTICE EXAMS

Latest Databricks Databricks-Certified-Data-Engineer-Associate Exam Vce, Databricks-Certified-Data-Engineer-Associate Free Practice Exams