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Databricks-Certified-Data-Engineer-Associate certification is a valuable credential for data engineers who want to advance their careers in the field of data processing and analytics. Databricks Certified Data Engineer Associate Exam certification demonstrates to employers that the holder has the skills and knowledge necessary to work with Databricks and can be trusted to manage and process large amounts of data in a variety of contexts. With the demand for skilled data engineers on the rise, obtaining the Databricks-Certified-Data-Engineer-Associate certification can help individuals differentiate themselves in a competitive job market.
The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a comprehensive examination that tests the skills of professionals who work with data on the Databricks platform. Databricks Certified Data Engineer Associate Exam certification is designed to help professionals stay up-to-date with the latest data engineering trends and technologies, and it can help professionals advance their career in the field of data engineering. By earning this certification, professionals can demonstrate their commitment to professional development and their dedication to staying current in their field.
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Databricks Certified Data Engineer Associate Exam Sample Questions (Q87-Q92):
NEW QUESTION # 87
An organization plans to share a large dataset stored in a Databricks workspace on AWS with a partner organization whose Databricks workspace is hosted on Azure. The data engineer wants to minimize data transfer costs while ensuring secure and efficient data sharing. Which strategy will reduce data egress costs associated with cross-cloud data sharing?
- A. Sharing data via pre-signed URLs without monitoring egress costs
- B. Migrating the dataset to Cloudflare R2 object storage before sharing
- C. Configure VPN connection between AWS and Azure for faster data sharing
- D. Using Delta Sharing without any additional configurations
Answer: D
Explanation:
Delta Sharing natively supports cross-cloud data access without requiring data duplication or special networking setups. It allows secure sharing of datasets across different cloud providers while minimizing egress costs, since recipients query the data directly from its source location rather than transferring large volumes unnecessarily.
NEW QUESTION # 88
A data engineer is migrating pipeline tasks to reduce operational toil. The workspace used Unity Catalog and is in a region that supports serverless. The engineer wants Databricks to auto-select instance types, manage scaling, apply Photon, and handle runtime upgrades automatically for job runs.
How should the data engineer meet this requirement while adhering to Databricks constraints?
- A. Run the job on a serverless compute for workflows configuration, ensuring Unity Catalog is enabled and regional support is available.
- B. Use a Pro SQL warehouse and schedule Python notebook tasks to execute as pipeline steps.
- C. Use an all-purpose cluster with cluster policies to enforce standard sizes and enable autoscaling.
- D. Create a job with a single-task job cluster and manually set instance families and min/max workers.
Answer: A
Explanation:
Serverless compute for workflows allows Databricks to automatically manage instance selection, scaling, Photon enablement, and runtime upgrades for job runs, provided Unity Catalog is enabled and the workspace region supports serverless.
NEW QUESTION # 89
A data engineer has a Python notebook in Databricks, but they need to use SQL to accomplish a specific task within a cell. They still want all of the other cells to use Python without making any changes to those cells.
Which of the following describes how the data engineer can use SQL within a cell of their Python notebook?
- A. They can change the default language of the notebook to SQL
- B. They can attach the cell to a SQL endpoint rather than a Databricks cluster
- C. They can add %sql to the first line of the cell
- D. They can simply write SQL syntax in the cell
- E. It is not possible to use SQL in a Python notebook
Answer: C
Explanation:
In Databricks, you can use different languages within the same notebook by using magic commands. Magic commands are special commands that start with a percentage sign (%) and allow you to change the behavior of the cell. To use SQL within a cell of a Python notebook, you can add %sql to the first line of the cell. This will tell Databricks to interpret the rest of the cell as SQL code and execute it against the default database. You can also specify a different database by using the USE statement. The result of the SQL query will be displayed as a table or a chart, depending on the output mode. You can also assign the result to a Python variable by using the -o option. For example, %sql -o df SELECT * FROM my_table will run the SQL query and store the result as a pandas DataFrame in the Python variable df. Option A is incorrect, as it is possible to use SQL in a Python notebook using magic commands. Option B is incorrect, as attaching the cell to a SQL endpoint is not necessary and will not change the language of the cell. Option C is incorrect, as simply writing SQL syntax in the cell will result in a syntax error, as the cell will still be interpreted as Python code. Option E is incorrect, as changing the default language of the notebook to SQL will affect all the cells, not just one. Reference: Use SQL in Notebooks - Knowledge Base - Noteable, [SQL magic commands - Databricks], [Databricks SQL Guide - Databricks]
NEW QUESTION # 90
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:
Which of the following lines of code fills in the above blank to successfully complete the task?
- A. USING CSV
- B. FROM "path/to/csv"
- C. FROM CSV
- D. None of these lines of code are needed to successfully complete the task
- E. USING DELTA
Answer: A
NEW QUESTION # 91
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 autoscale their work as data size evolves
- B. Both teams would respond more quickly to ad-hoc requests
- C. Both teams would be able to collaborate on projects in real-time
- D. Both teams would use the same source of truth for their work
- E. Both teams would reorganize to report to the same department
Answer: D
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. Reference: 1: What is a Data Lakehouse? - Databricks 2: What is a data lakehouse? | IBM
NEW QUESTION # 92
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