Guaranteed Databricks-Certified-Data-Engineer-Associate Questions Answers, Valid Databricks-Certified-Data-Engineer-Associate Vce

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Databricks Databricks-Certified-Data-Engineer-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Data Engineer Associate Exam
Exam Number:	Databricks-Certified-Data-Engineer-Associate
Exam Duration:	90 minutes
Exam Price:	$200 USD
Passing Score:	70%
Exam Format:	Multiple Select, Multiple Choice
Related Certifications:	Databricks Certified Data Analyst Associate
Real Exam Qty:	60
Available Languages:	English
Certificate Validity Period:	2 years
Sample Questions:	Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions
Exam Way:	Online proctored or in-person testing center
Pre Condition:	Recommended: 6+ months of experience with Databricks and Apache Spark
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-associate

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Databricks Certified Data Engineer Associate Exam is a vendor-neutral certification that tests candidates on their understanding of Databricks architecture and features, data ingestion and processing, data transformation and storage, and machine learning. Databricks-Certified-Data-Engineer-Associate Exam also covers important concepts such as performance tuning, security, and troubleshooting in Databricks. Passing the exam demonstrates that a candidate has the skills, knowledge, and expertise to work with Databricks to design, build, and maintain advanced data pipelines and solutions.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q96-Q101):

NEW QUESTION # 96
A single Job runs two notebooks as two separate tasks. A data engineer has noticed that one of the notebooks is running slowly in the Job's current run. The data engineer asks a tech lead for help in identifying why this might be the case.
Which of the following approaches can the tech lead use to identify why the notebook is running slowly as part of the Job?

A. They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.
B. They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook.
C. They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.
D. They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.
E. There is no way to determine why a Job task is running slowly.

Answer: B

NEW QUESTION # 97
A data engineer has manually created several Databricks jobs and dashboards using the workspace UI. The team now wants to manage these resources as code using Declarative Automation Bundles (formerly Databricks Asset Bundles), store the configuration in a Git repository, and deploy changes through CI/CD. Which approach converts the existing resources into a bundle project?

A. Use the Databricks REST API to export each job's JSON definition, save the JSON files into a Git repository, and configure CI/CD to import them using the REST API on each deployment.
B. Run databricks bundle init to create a new bundle project, then use databricks bundle deploy pointed at the workspace to automatically detect and import all existing resources into the bundle configuration.
C. Clone an empty Git repository into a Git Folder, recreate each job and dashboard configuration in YAML by referencing the settings using workspace UI, then commit and deploy with databricks bundle deploy.
D. Run databricks bundle generate for each existing resource, use --bind to link the generated configuration to the existing workspace resources, and commit the bundle project to Git for CI/CD deployment.

Answer: D

Explanation:
databricks bundle generate creates bundle configuration from each existing job or dashboard.
Using --bind links that configuration to the existing workspace resource, allowing subsequent bundle deployments to update it rather than create a duplicate.

NEW QUESTION # 98
A data engineer manages multiple external tables linked to various data sources. The data engineer wants to manage these external tables efficiently and ensure that only the necessary permissions are granted to users for accessing specific external tables.
How should the data engineer manage access to these external tables?

A. Create a single user role with full access to all external tables and assign it to all users.
B. Grant permissions on the Databricks workspace level, which will automatically apply to all external tables.
C. Set up Azure Blob Storage permissions at the container level, allowing access to all external tables.
D. Use Unity Catalog to manage access controls and permissions for each external table individually.

Answer: D

NEW QUESTION # 99
Which of the following describes the relationship between Gold tables and Silver tables?

A. Gold tables are more likely to contain aggregations than Silver tables.
B. Gold tables are more likely to contain a less refined view of data than Silver tables.
C. Gold tables are more likely to contain truthful data than Silver tables.
D. Gold tables are more likely to contain more data than Silver tables.
E. Gold tables are more likely to contain valuable data than Silver tables.

Answer: B

NEW QUESTION # 100
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?

A. Both teams would use the same source of truth for their work
B. Both teams would reorganize to report to the same department
C. Both teams would autoscale their work as data size evolves
D. Both teams would respond more quickly to ad-hoc requests
E. Both teams would be able to collaborate on projects in real-time

Answer: A

Explanation:
A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. By using a data lakehouse, both the data analysis and data engineering teams can access the same data sources and formats, ensuring data consistency and quality across their reports. A data lakehouse also supports schema enforcement and evolution, data validation, and time travel to old table versions, which can help resolve data conflicts and errors1. References: 1: What is a Data Lakehouse? - Databricks 2: What is a data lakehouse? | IBM

NEW QUESTION # 101
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