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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a comprehensive and rigorous examination that validates a candidate's understanding of the Databricks platform and its role in data engineering. Databricks Certified Data Engineer Associate Exam certification focuses on assessing the practical skills and knowledge required to design, build, and maintain data pipelines using Databricks.

Databricks Certified Data Engineer Associate Exam is a comprehensive exam that covers a wide range of topics related to data engineering using Databricks. Databricks-Certified-Data-Engineer-Associate Exam consists of multiple-choice questions that test an individual's understanding of Databricks fundamentals, data ingestion, data transformation, data analysis, and performance optimization. Databricks-Certified-Data-Engineer-Associate exam is designed to be challenging and requires a deep understanding of Databricks and data engineering concepts.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q182-Q187):

NEW QUESTION # 182
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 table can be deleted
B. When the location of the data needs to be changed
C. When the target table cannot contain duplicate records
D. When the source is not a Delta table
E. When the target table is an external table

Answer: C

Explanation:
The MERGE INTO command is used to perform upserts, which are a combination of insertions and updates, based on a source table into a target Delta table1. The MERGE INTO command can handle scenarios where the target table cannot contain duplicate records, such as when there is a primary key or a unique constraint on the target table. The MERGE INTO command can match the source and target rows based on a merge condition and perform different actions depending on whether the rows are matched or not. For example, the MERGE INTO command can update the existing target rows with the new source values, insert the new source rows that do not exist in the target table, or delete the target rows that do not exist in the source table1.
The INSERT INTO command is used to append new rows to an existing table or create a new table from a query result2. The INSERT INTO command does not perform any updates or deletions on the existing target table rows. The INSERT INTO command can handle scenarios where the location of the data needs to be changed, such as when the data needs to be moved from one table to another, or when the data needs to be partitioned by a certain column2. The INSERT INTO command can also handle scenarios where the target table is an external table, such as when the data is stored in an external storage system like Amazon S3 or Azure Blob Storage3. The INSERT INTO command can also handle scenarios where the source table can be deleted, such as when the source table is a temporary table or a view4. The INSERT INTO command can also handle scenarios where the source is not a Delta table, such as when the source is a Parquet, CSV, JSON, or Avro file5.
:
1: MERGE INTO | Databricks on AWS
2: [INSERT INTO | Databricks on AWS]
3: [External tables | Databricks on AWS]
4: [Temporary views | Databricks on AWS]
5: [Data sources | Databricks on AWS]

NEW QUESTION # 183
Which single Databricks CLI command deploys local bundle assets to the target workspace specified in the configuration file?

A. databricks bundle validate
B. databricks bundle init && databricks bundle deployment
C. databricks bundle plan && databricks bundle run
D. databricks bundle deploy

Answer: D

Explanation:
databricks bundle deploy deploys the local bundle files and resources to the target workspace defined in the bundle configuration.

NEW QUESTION # 184
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
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 turn on the Auto Stop feature for the SQL endpoint.
B. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
C. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
D. They can set up the dashboard's SQL endpoint to be serverless.
E. They can reduce the cluster size of the SQL endpoint.

Answer: D

Explanation:
A serverless SQL endpoint is a compute resource that is automatically managed by Databricks and scales up or down based on the workload. A serverless SQL endpoint can be used to run queries and dashboards without requiring manual configuration or management. A serverless SQL endpoint is only active when it is needed and shuts down automatically when idle, minimizing the total running time and cost. A serverless SQL endpoint can be created and assigned to a dashboard using the Databricks SQL UI or the SQL Analytics API. References:
* Create a serverless SQL endpoint
* Assign a SQL endpoint to a dashboard
* SQL Analytics API

NEW QUESTION # 185
A data engineer is designing an ETL pipeline to process both streaming and batch data from multiple sources. The pipeline must ensure data quality, handle schema evolution, and provide easy maintenance. The team is considering using Delta Live Tables (DLT) in Databricks to achieve these goals. They want to understand the key features and benefits of DLT that make it suitable for this use case. Why is Delta Live Tables (DLT) an appropriate choice?

A. Requires custom code for data quality checks, no support for streaming data, and complex pipeline maintenance
B. Automatic data quality checks, built-in support for schema evolution, and declarative pipeline development
C. Manual schema enforcement, high operational overhead, and limited scalability
D. Supports only batch processing, no data versioning, and high infrastructure costs

Answer: B

Explanation:
Delta Live Tables (DLT) is appropriate because it provides automatic data quality checks through expectations, built-in support for schema evolution, and a declarative approach to pipeline development, making it easier to build and maintain both streaming and batch ETL pipelines.

NEW QUESTION # 186
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

A. Parquet files have the ability to be optimized
B. CREATE TABLE AS SELECT statements cannot be used on files
C. Parquet files have a well-defined schema
D. Parquet files will become Delta tables
E. Parquet files can be partitioned

Answer: C

Explanation:
Option C is the correct answer because Parquet files have a well-defined schema that is embedded within the data itself. This means that the data types and column names of the Parquet files are automatically detected and preserved when creating an external table from them. This also enables the use of SQL and other structured query languages to access and analyze the data. CSV files, on the other hand, do not have a schema embedded in them, and require specifying the schema explicitly or inferring it from the data when creating an external table from them. This can lead to errors or inconsistencies in the data types and column names, and also increase the processing time and complexity.
References: CREATE TABLE AS SELECT, Parquet Files, CSV Files, Parquet vs. CSV

NEW QUESTION # 187
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