Valid Exam Databricks-Certified-Data-Engineer-Associate Registration | Databricks-Certified-Data-Engineer-Associate Exam Format

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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Lakehouse Platform Concepts	10-15%	- Explain data governance and security concepts - Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits
Apache Spark Data Processing Fundamentals	20-25%	- Work with structured data types (arrays, maps, structs) - Apply transformations and actions on DataFrames - Create and use Spark DataFrames - Use Spark SQL for data processing
Python for Data Engineering	10-15%	- Work with Spark APIs in Python - Implement user-defined functions (UDFs) - Use PySpark for data processing
Spark SQL and DataFrames	15-20%	- Write and execute Spark SQL queries - Join and union DataFrames - Handle null values and data quality - Aggregate and group data
Data Pipeline Architecture	15-20%	- Understand ELT vs ETL patterns - Implement incremental data processing - Design data pipelines for batch and streaming - Monitor and optimize pipeline performance
Delta Lake Fundamentals	20-25%	- Understand ACID transactions and time travel - Create and manage Delta tables - Write to and read from Delta tables - Explain Delta Lake features and benefits

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q83-Q88):

NEW QUESTION # 83
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?

A. Databricks Filesystem
B. Data Explorer
C. Jobs
D. Dashboards
E. Repos

Answer: B

Explanation:
Data Explorer is a graphical interface that allows you to browse, create, and manage data objects such as databases, tables, and views in your workspace. You can also review and modify the permissions on these data objects using Data Explorer. To access Data Explorer, you can click on the Data icon in the sidebar, or use the %sql magic command in a notebook. You can then select a database and a table, and click on the Permissions tab to view and edit the access control lists (ACLs) for the table. You can also use SQL commands such as SHOW GRANT and GRANT to query and modify the permissions on a Delta table. References:
* Data Explorer
* Access control for Delta tables
* SHOW GRANT
* [GRANT]

NEW QUESTION # 84
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace format("delta") with format("stream")
B. Replace predict with a stream-friendly prediction function
C. Replace "transactions" with the path to the location of the Delta table
D. Replace spark.read with spark.readStream
E. Replace schema(schema) with option ("maxFilesPerTrigger", 1)

Answer: D

Explanation:
To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. References: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources

NEW QUESTION # 85
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 once and the pipeline will persist without any processing. The compute resources will persist but go unused.
E. 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.

Answer: A

NEW QUESTION # 86
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
MERGE SELECT * FROM april_transactions;
C. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
OUTER JOIN SELECT * FROM april_transactions;
D. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INNER JOIN SELECT * FROM april_transactions;
E. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION SELECT * FROM april_transactions;

Answer: E

NEW QUESTION # 87
A data engineer notices that a Spark job performing a join between a large table and a small lookup table is slow. The lookup table is only a few megabytes. Which Spark optimization technique should be applied to improve the performance of the join operation?

A. Broadcast join
B. Shuffle join
C. Cartesian join
D. Sort merge join

Answer: A

NEW QUESTION # 88
......

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