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Databricks Certified Data Engineer Associate certification demonstrates that you have the skills and knowledge required to build and manage big data processing systems using Databricks. Databricks Certified Data Engineer Associate Exam certification is ideal for data engineers, data architects, and developers who work with big data workloads. It validates your ability to design, build, and maintain scalable and reliable data processing pipelines using Databricks.

The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a prestigious certification exam that is designed for professionals who have experience in designing and implementing big data solutions using Databricks platform. Databricks Certified Data Engineer Associate Exam certification exam is ideal for data engineers, architects, developers, and data scientists who want to demonstrate their skills and knowledge in working with Databricks for big data processing and analytics.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
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 set up the dashboard's SQL endpoint to be serverless.
B. They can reduce the cluster size of the SQL endpoint.
C. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
D. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
E. They can turn on the Auto Stop feature for the SQL endpoint.

Answer: E

Explanation:
The Auto Stop feature allows the SQL endpoint to automatically stop after a specified period of inactivity. This can help reduce the cost and resource consumption of the SQL endpoint, as it will only run when it is needed to refresh the dashboard or execute queries. The data engineer can configure the Auto Stop setting for the SQL endpoint from the SQL Endpoints UI, by selecting the desired idle time from the Auto Stop dropdown menu. The default idle time is 120 minutes, but it can be set to as low as 15 minutes or as high as 240 minutes. Alternatively, the data engineer can also use the SQL Endpoints REST API to set the Auto Stop setting programmatically. Reference: SQL Endpoints UI, SQL Endpoints REST API, Refreshing SQL Dashboard

NEW QUESTION # 72
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

A. The pipeline will need to stop using the medallion-based multi-hop architecture
B. None of these changes will need to be made
C. The pipeline will need to be written entirely in Python
D. The pipeline will need to use a batch source in place of a streaming source
E. The pipeline will need to be written entirely in SQL

Answer: B

Explanation:
Delta Live Tables is a declarative framework for building reliable, maintainable, and testable data processing pipelines. You define the transformations to perform on your data and Delta Live Tables manages task orchestration, cluster management, monitoring, data quality, and error handling. Delta Live Tables supports both SQL and Python as the languages for defining your datasets and expectations. Delta Live Tables also supports both streaming and batch sources, and can handle both append-only and upsert data patterns. Delta Live Tables follows the medallion lakehouse architecture, which consists of three layers of data: bronze, silver, and gold. Therefore, migrating to Delta Live Tables does not require any of the changes listed in the options B, C, D, or E. The data engineer and data analyst can use the same languages, sources, and architecture as before, and simply declare their datasets and expectations using Delta Live Tables syntax. Reference:
What is Delta Live Tables?
Transform data with Delta Live Tables
What is the medallion lakehouse architecture?

NEW QUESTION # 73
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 set up the dashboard's SQL endpoint to be serverless.
B. They can reduce the cluster size of the SQL endpoint.
C. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
D. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
E. They can turn on the Auto Stop feature for the SQL endpoint.

Answer: E

NEW QUESTION # 74
A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?

A. Commit
B. Clone
C. Merge
D. Push
E. Pull

Answer: E

Explanation:
To sync a Databricks Repo with the changes from a central Git repository, the data engineer needs to run the Git pull operation. This operation fetches the latest updates from the remote repository and merges them with the local repository. The data engineer can use the Pull button in the Databricks Repos UI, or use the git pull command in a terminal session. The other options are not relevant for this task, as they either push changes to the remote repository (Push), combine two branches (Merge), save changes to the local repository (Commit), or create a new local repository from a remote one (Clone). References:
* Run Git operations on Databricks Repos
* Git pull

NEW QUESTION # 75
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION DROP ROW What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
B. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
C. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
D. Records that violate the expectation cause the job to fail.
E. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.

Answer: C

Explanation:
Delta Live Tables expectations are optional clauses that apply data quality checks on each record passing through a query. An expectation consists of a description, a boolean statement, and an action to take when a record fails the expectation. The ON VIOLATION clause specifies the action to take, which can be one of the following: warn, drop, or fail. The drop action means that invalid records are dropped from the target dataset before data is written to the target. The failure is reported as a metric for the dataset, which can be viewed by querying the Delta Live Tables event log. The event log contains information such as the number of records that violate an expectation, the number of records dropped, and the number of records written to the target dataset. References:
* Manage data quality with Delta Live Tables
* Monitor Delta Live Tables pipelines
* Delta Live Tables SQL language reference

NEW QUESTION # 76
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Valid Databricks-Certified-Data-Engineer-Associate Real Test | New Databricks-Certified-Data-Engineer-Associate Cram Materials

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q71-Q76):

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