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Free PDF Quiz Professional Databricks - Databricks-Certified-Data-Engineer-Associate - Latest Databricks Certified Data Engineer Associate Exam Exam Questions

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To take the Databricks-Certified-Data-Engineer-Associate exam, individuals must have a good understanding of data engineering concepts and experience working with Databricks. Databricks-Certified-Data-Engineer-Associate exam consists of multiple-choice questions that cover a wide range of topics, including data ingestion, data transformation, data storage, and data analysis. Candidates must score a passing grade to earn the certification.

The GAQM Databricks-Certified-Data-Engineer-Associate Exam consists of multiple-choice questions that cover various topics related to data engineering using Databricks. Databricks-Certified-Data-Engineer-Associate exam tests a candidate's knowledge of data engineering concepts, big data processing and analytics, and cloud computing using Databricks. Candidates are required to pass the exam to earn the Databricks Certified Data Engineer Associate certification.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

  • A. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
  • B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
  • C. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
  • D. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
  • E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.

Answer: E

Explanation:
The Continuous Pipeline Mode for Delta Live Tables allows the pipeline to run continuously and process data as it arrives. This mode is suitable for streaming ingest and CDC workloads that require low-latency updates.
The Development mode for Delta Live Tables allows the pipeline to run on a dedicated cluster that is not shared with other pipelines. This mode is useful for testing and debugging the pipeline logic before deploying it to production. Therefore, the correct answer is B, because the pipeline will run continuously on a dedicated cluster until it is manually stopped, and the compute resources will be released only after the pipeline is shut down. References: Databricks Documentation - Configure pipeline settings for Delta Live Tables, Databricks Documentation - Continuous vs. triggered pipeline execution, Databricks Documentation - Development vs.
production mode.


NEW QUESTION # 13
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 be able to collaborate on projects in real-time
  • B. Both teams would autoscale their work as data size evolves
  • C. Both teams would reorganize to report to the same department
  • D. Both teams would respond more quickly to ad-hoc requests
  • E. Both teams would use the same source of truth for their work

Answer: E

Explanation:
Explanation
A data lakehouse is designed to unify the data engineering and data analysis architectures by integrating features of both data lakes and data warehouses. One of the key benefits of a data lakehouse is that it provides a common, centralized data repository (the "lake") that serves as a single source of truth for data storage and analysis. This allows both data engineering and data analysis teams to work with the same consistent data sets, reducing discrepancies and ensuring that the reports generated by both teams are based on the same underlying data.


NEW QUESTION # 14
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:

If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?

  • A. trigger(processingTime="once")
  • B. trigger(continuous="once")
  • C. processingTime(1)
  • D. trigger(parallelBatch=True)
  • E. trigger(availableNow=True)

Answer: E

Explanation:
https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter


NEW QUESTION # 15
Which of the following describes the relationship between Bronze tables and raw data?

  • A. Bronze tables contain raw data with a schema applied.
  • B. Bronze tables contain aggregates while raw data is unaggregated.
  • C. Bronze tables contain more truthful data than raw data.
  • D. Bronze tables contain less data than raw data files.
  • E. Bronze tables contain a less refined view of data than raw data.

Answer: B


NEW QUESTION # 16
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

  • A.
  • B.
  • C.
  • D.
  • E.

Answer: E

Explanation:
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/


NEW QUESTION # 17
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