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Databricks-Certified-Data-Engineer-Associate Exam tests the candidates' abilities to perform the necessary tasks needed to become a successful data engineer. Databricks-Certified-Data-Engineer-Associate exam covers a wide range of topics such as data extraction, transformation, and loading (ETL), data modeling, data warehousing, and machine learning. Individuals who have passed Databricks-Certified-Data-Engineer-Associate exam possess the professional knowledge necessary to integrate, manipulate, secure, and monitor data within Databricks.
The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is designed for professionals who are interested in validating their expertise in building and managing big data processing systems using Databricks. Databricks is a unified data analytics platform that offers a cloud-based environment for processing big data workloads. Databricks-Certified-Data-Engineer-Associate exam covers a wide range of topics, including data engineering, data processing, data storage, and data analysis.
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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) certification exam is a credential that certifies individuals in their ability to design, develop, and manage data pipelines using Databricks. It is a globally recognized certification that validates the skills and knowledge required to work with big data processing and analytics using Databricks.
Databricks Certified Data Engineer Associate Exam Sample Questions (Q126-Q131):
NEW QUESTION # 126
In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?
- A. When the source is not a Delta table
- B. When the location of the data needs to be changed
- C. When the target table is an external table
- D. When the target table cannot contain duplicate records
- E. When the source table can be deleted
Answer: D
NEW QUESTION # 127
In a Lakeflow Declarative Pipeline, a data engineer needs one dataset that appends new Kafka records incrementally and another that always reflects the full, recomputed result of a join across two dimensions.
Which dataset types should be used, respectively?
- A. Streaming table and materialized view
- B. Streaming table and streaming table
- C. Materialized view and streaming table
- D. Temporary view and external table
Answer: A
Explanation:
A streaming table processes each input record exactly once from an append-only source, which suits incremental Kafka ingestion. A materialized view returns results based on the current state of its sources and is recomputed as inputs change, which suits the dimensional join.
NEW QUESTION # 128
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?
- A. COMMENT "Contains PII"
- B. There is no way to indicate whether a table contains PII.
- C. TBLPROPERTIES PII
- D. "COMMENT PII"
- E. PII
Answer: A
Explanation:
In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it's organization policy to indicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon. References: Data Engineer Associate Exam Guide, CREATE TABLE USING (Databricks SQL)
NEW QUESTION # 129
A data architect has determined that a table of the following format is necessary:
Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

- A. Option C
- B. Option A
- C. Option D
- D. Option E
- E. Option B
Answer: D
NEW QUESTION # 130
Which of the following must be specified when creating a new Delta Live Tables pipeline?
- A. The preferred DBU/hour cost
- B. A key-value pair configuration
- C. A location of a target database for the written data
- D. At least one notebook library to be executed
- E. A path to cloud storage location for the written data
Answer: D
Explanation:
Option E is the correct answer because it is the only mandatory requirement when creating a new Delta Live Tables pipeline. A pipeline is a data processing workflow that contains materialized views and streaming tables declared in Python or SQL source files. Delta Live Tables infers the dependencies between these tables and ensures updates occur in the correct order. To create a pipeline, you need to specify at least one notebook library to be executed, which contains the Delta Live Tables syntax. You can also specify multiple libraries of different languages within your pipeline. The other options are optional or not applicable for creating a pipeline. Option A is not required, but you can optionally provide a key-value pair configuration to customize the pipeline settings, such as the storage location, the target schema, the notifications, and the pipeline mode. Option B is not applicable, as the DBU/hour cost is determined by the cluster configuration, not the pipeline creation. Option C is not required, but you can optionally specify a storage location for the output data from the pipeline. If you leave it empty, the system uses a default location. Option D is not required, but you can optionally specify a location of a target database for the written data, either in the Hive metastore or the Unity Catalog.
NEW QUESTION # 131
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