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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Engineer Associate Exam |
| Exam Number: | Databricks-Certified-Data-Engineer-Associate |
| Exam Price: | 200 USD |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 45 |
| Related Certifications: | Databricks Certified Data Analyst Associate Databricks Certified Data Engineer Professional |
| Exam Duration: | 90 minutes |
| Exam Format: | Multiple choice, Proctored |
| Passing Score: | Not publicly disclosed, approximately 70% |
| Available Languages: | Portuguese (Brazil), English, Japanese, Korean |
| Recommended Training: | Databricks Documentation Databricks Academy - Data Engineer Associate Learning Path |
| Exam Registration: | Official Registration |
| Sample Questions: | Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions |
| Exam Way: | Online proctored or in-person at authorized test centers |
| Pre Condition: | No mandatory prerequisites; 6+ months hands-on experience with Databricks and data engineering tasks recommended |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-associate |
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Databricks Certified Data Engineer Associate exam is a comprehensive and rigorous assessment of a data engineer's proficiency in Databricks. Databricks-Certified-Data-Engineer-Associate Exam covers a wide range of topics, including Databricks architecture, data ingestion, data processing, data engineering, data storage, and data management. It is designed to test the candidate's knowledge of Databricks best practices and their ability to implement these practices in real-world scenarios.
NEW QUESTION # 53
Which SQL keyword can be used to convert a table from a long format to a wide format?
Answer: C
NEW QUESTION # 54
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?
Answer: C
NEW QUESTION # 55
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?
Answer: B
Explanation:
A data lakehouse is a new paradigm that can be used to simplify and unify siloed data architectures that are specialized for specific use cases. A data lakehouse combines the best of both data lakes and data warehouses, providing a single platform that supports diverse data types, open standards, low-cost storage, high-performance queries, ACID transactions, schema enforcement, and governance. A data lakehouse enables data engineers to build reliable and scalable data pipelines that can serve various downstream applications and users, such as data science, machine learning, analytics, and reporting. A data lakehouse leverages the power of Delta Lake, a storage layer that brings reliability and performance to data lakes. References: What is a data lakehouse?, Delta Lake, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics
NEW QUESTION # 56
After a failed multi-task job run, a data engineer changes the cluster size and updates a notebook path for one task. The data engineer wants to re-run just the failed/skipped tasks with the updated settings and keep the run lineage intact. Which strategy should the data engineer use to preserve the run history?
Answer: A
Explanation:
The repairRun capability allows rerunning only failed or skipped tasks while preserving the original run lineage, and it applies the current job and task settings during the repair execution.
NEW QUESTION # 57
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?
Answer: D
Explanation:
Option D is the correct answer because it enables the Serverless feature for the SQL endpoint, which allows the endpoint to automatically scale up and down based on the query load. This way, the endpoint can handle more concurrent queries and reduce the time it takes to return results. The Serverless feature also reduces the cold start time of the endpoint, which is the time it takes to start the cluster when a query is submitted to a non-running endpoint. The Serverless feature is available for both AWS and Azure Databricks platforms.
NEW QUESTION # 58
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