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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a comprehensive and rigorous examination that validates a candidate's understanding of the Databricks platform and its role in data engineering. Databricks Certified Data Engineer Associate Exam certification focuses on assessing the practical skills and knowledge required to design, build, and maintain data pipelines using Databricks.

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a highly sought-after certification for data professionals. Databricks Certified Data Engineer Associate Exam certification is designed to test the knowledge and skills of individuals who work with big data and data engineering. Databricks-Certified-Data-Engineer-Associate Exam covers a wide range of topics, including data modeling, ETL processes, data warehousing, and data analysis.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q267-Q272):

NEW QUESTION # 267
A data engineer works for an organization that must meet a stringent Service Level Agreement (SLA) that demands minimal runtime errors and high availability for its data processing pipelines. The data engineer wants to avoid the operational overhead of managing and tuning clusters.
Which architectural solution will meet the requirements?

Answer: C


NEW QUESTION # 268
A data engineering team is using Kafka to capture event data and then ingest it into Databricks. The team wants to be able to see these historical events. Medallion architecture is already in place. The team wants to be mindful of costs.
Where should this historical event data be stored?

Answer: B


NEW QUESTION # 269
Which of the following describes a scenario in which a data team will want to utilize cluster pools?

Answer: C

Explanation:
Explanation
Cluster pools are typically used in distributed computing environments, such as cloud-based data platforms like Databricks. They allow you to pre-allocate a set of compute resources (a cluster) for specific tasks or workloads. In this case, if an automated report needs to be refreshed as quickly as possible, you can allocate a cluster pool with sufficient resources to ensure fast data processing and report generation. This helps ensure that the report is generated with minimal latency and can be delivered to stakeholders in a timely manner.
Cluster pools allow you to optimize resource allocation for high-demand, time-sensitive tasks like real-time report generation.


NEW QUESTION # 270
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

Answer: B

Explanation:
To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.
read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. References: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources


NEW QUESTION # 271
A data engineer needs to control access to data assets across multiple workspaces and enforce centralized governance policies. The organization wants fine-grained access control for tables, schemas, and catalogs. Which Databricks feature supports this requirement?

Answer: C


NEW QUESTION # 272
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