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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a certification exam that tests the skills and knowledge of individuals in the field of data engineering using Databricks. Databricks is a cloud-based data platform that allows data engineers to build, manage, and deploy data pipelines at scale. Databricks-Certified-Data-Engineer-Associate Exam is designed to assess an individual's proficiency in using Databricks to perform tasks such as data transformation, data ingestion, and data analysis.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

Answer: B

Explanation:
The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

NEW QUESTION # 90
A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?

A. The table was managed
B. The table's data was smaller than 10 GB
C. The table was external
D. The table did not have a location
E. The table's data was larger than 10 GB

Answer: A

Explanation:
Explanation
managed tables files and metadata are managed by metastore and will be deleted when the table is dropped .
while external tables the metadata is stored in a external location. hence when a external table is dropped you clear off only the metadata and the files (data) remain.

NEW QUESTION # 91
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?

A. The ability to collaborate in real time on a single notebook
B. The ability to support batch and streaming workloads
C. The ability to distribute complex data operations
D. The ability to manipulate the same data using a variety of languages
E. The ability to set up alerts for query failures

Answer: B

Explanation:
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale1. Delta Lake supports upserts using the merge operation, which enables you to efficiently update existing data or insert new data into your Delta tables2. Delta Lake also provides time travel capabilities, which allow you to query previous versions of your data or roll back to a specific point in time3. Reference: 1: What is Delta Lake? | Databricks on AWS 2: Upsert into a table using merge | Databricks on AWS 3: [Query an older snapshot of a table (time travel) | Databricks on AWS] Learn more

NEW QUESTION # 92
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 allows users to revert to previous versions of a notebook
B. Databricks Repos automatically saves development progress
C. Databricks Repos is wholly housed within the Databricks Lakehouse Platform
D. Databricks Repos provides the ability to comment on specific changes
E. Databricks Repos supports the use of multiple branches

Answer: E

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 # 93
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?

A. They can set up an Alert with a new webhook alert destination.
B. They can set up an Alert without notifications.
C. They can set up an Alert with a new email alert destination.
D. They can set up an Alert with a custom template.
E. They can set up an Alert with one-time notifications.

Answer: A

NEW QUESTION # 94
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