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Databricks Certified Data Engineer Associate Exam Sample Questions (Q104-Q109):

NEW QUESTION # 104
A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.
Which of the following commands can be used to grant the necessary permission on the entire database to the new team?

A. GRANT USAGE ON DATABASE customers TO team;
B. GRANT USAGE ON CATALOG team TO customers;
C. GRANT VIEW ON CATALOG customers TO team;
D. GRANT CREATE ON DATABASE team TO customers;
E. GRANT CREATE ON DATABASE customers TO team;

Answer: A

Explanation:
The correct command to grant the necessary permission on the entire database to the new team is to use the GRANT USAGE command. The GRANT USAGE command grants the principal the ability to access the securable object, such as a database, schema, or table. In this case, the securable object is the database customers, and the principal is the group team. By granting usage on the database, the team will be able to see what tables already exist in the database. Option E is the only option that uses the correct syntax and the correct privilege type for this scenario. Option A uses the wrong privilege type (VIEW) and the wrong securable object (CATALOG). Option B uses the wrong privilege type (CREATE), which would allow the team to create new tables in the database, but not necessarily see the existing ones. Option C uses the wrong securable object (CATALOG) and the wrong principal (customers). Option D uses the wrong securable object (team) and the wrong principal (customers). References: GRANT, Privilege types, Securable objects, Principals

NEW QUESTION # 105
A data engineer has been given a new record of data:
id STRING = 'a1'
rank INTEGER = 6
rating FLOAT = 9.4
Which of the following SQL commands can be used to append the new record to an existing Delta table my_table?

A. my_table UNION VALUES ('a1', 6, 9.4)
B. UPDATE my_table VALUES ('a1', 6, 9.4)
C. INSERT INTO my_table VALUES ('a1', 6, 9.4)
D. INSERT VALUES ( 'a1' , 6, 9.4) INTO my_table
E. UPDATE VALUES ('a1', 6, 9.4) my_table

Answer: C

NEW QUESTION # 106
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 persist to allow for additional testing.
B. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
C. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
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: E

Explanation:
1: In Production mode, the pipeline runs continuously and updates the output tables whenever new data is available in the input sources. The compute resources are allocated on demand and released when the pipeline is stopped. This mode is suitable for production workloads that require high availability and reliability. Reference: Configure pipeline settings for Delta Live Tables, Tutorial: Run your first Delta Live Tables pipeline, Building Reliable Data Pipelines Using DataBricks' Delta Live Tables

NEW QUESTION # 107
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 Development 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 be terminated.
B. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.
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 persist to allow for additional testing.

Answer: E

Explanation:
Explanation
You can optimize pipeline execution by switching between development and production modes. Use the Delta Live Tables Environment Toggle Icon buttons in the Pipelines UI to switch between these two modes. By default, pipelines run in development mode.
When you run your pipeline in development mode, the Delta Live Tables system does the following:
Reuses a cluster to avoid the overhead of restarts. By default, clusters run for two hours when development mode is enabled. You can change this with the pipelines.clusterShutdown.delay setting in the Configure your compute settings.
Disables pipeline retries so you can immediately detect and fix errors.
In production mode, the Delta Live Tables system does the following:
Restarts the cluster for specific recoverable errors, including memory leaks and stale credentials.
Retries execution in the event of specific errors, for example, a failure to start a cluster.
https://docs.databricks.com/en/delta-live-tables/updates.html#optimize-execution

NEW QUESTION # 108
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?

A. Databricks Repos provides the ability to comment on specific changes
B. Databricks Repos supports the use of multiple branches
C. Databricks Repos automatically saves development progress
D. Databricks Repos is wholly housed within the Databricks Lakehouse Platform
E. Databricks Repos allows users to revert to previous versions of a notebook

Answer: B

Explanation:
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. References: Git integration with Databricks Repos - Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available - New 'Files' Feature in ..., Databricks Repos - What it is and how we can use it | Adatis.

NEW QUESTION # 109
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