DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE VALID STUDY MATERIALS - LATEST BRAINDUMPS DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE PPT

Databricks-Certified-Data-Engineer-Associate Valid Study Materials - Latest Braindumps Databricks-Certified-Data-Engineer-Associate Ppt

Databricks-Certified-Data-Engineer-Associate Valid Study Materials - Latest Braindumps Databricks-Certified-Data-Engineer-Associate Ppt

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The GAQM Databricks Certified Data Engineer Associate (Databricks-Certified-Data-Engineer-Associate) exam is a certification program designed for professionals who want to validate their expertise in data engineering with Databricks. Databricks is a cloud-based data processing and analytics platform that helps organizations to accelerate innovation and achieve better business outcomes. Databricks-Certified-Data-Engineer-Associate Exam is designed to test the candidate's proficiency in performing various data engineering tasks using Databricks.

Databricks Certified Data Engineer Associate exam measures an individual's ability to design and implement data pipelines, optimize and tune big data solutions, and manage data workflows using Databricks. Candidates are expected to have a solid understanding of data processing techniques, data storage, and data management concepts. Databricks Certified Data Engineer Associate Exam certification program offers a comprehensive assessment of an individual's skills in the field of big data engineering and provides a valuable credential for those looking to advance their careers in this field.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q92-Q97):

NEW QUESTION # 92
A data engineer has been given a new record of data:
id STRING = 'a1'
rank INTEGER = 6
rating FLOAT = 9.4
Which of the following SQL commands can be used to append the new record to an existing Delta table my_table?

  • A. INSERT INTO my_table VALUES ('a1', 6, 9.4)
  • B. my_table UNION VALUES ('a1', 6, 9.4)
  • C. UPDATE my_table VALUES ('a1', 6, 9.4)
  • D. UPDATE VALUES ('a1', 6, 9.4) my_table
  • E. INSERT VALUES ( 'a1' , 6, 9.4) INTO my_table

Answer: A


NEW QUESTION # 93
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

  • A. The pipeline will need to use a batch source in place of a streaming source
  • B. The pipeline will need to be written entirely in Python
  • C. The pipeline will need to stop using the medallion-based multi-hop architecture
  • D. The pipeline will need to be written entirely in SQL
  • E. None of these changes will need to be made

Answer: E

Explanation:
Delta Live Tables is a declarative framework for building reliable, maintainable, and testable data processing pipelines. You define the transformations to perform on your data and Delta Live Tables manages task orchestration, cluster management, monitoring, data quality, and error handling. Delta Live Tables supports both SQL and Python as the languages for defining your datasets and expectations. Delta Live Tables also supports both streaming and batch sources, and can handle both append-only and upsert data patterns. Delta Live Tables follows the medallion lakehouse architecture, which consists of three layers of data: bronze, silver, and gold. Therefore, migrating to Delta Live Tables does not require any of the changes listed in the options B, C, D, or E. The data engineer and data analyst can use the same languages, sources, and architecture as before, and simply declare their datasets and expectations using Delta Live Tables syntax. References:
* What is Delta Live Tables?
* Transform data with Delta Live Tables
* What is the medallion lakehouse architecture?


NEW QUESTION # 94
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

  • A.
  • B.
  • C.
  • D.
  • E.

Answer: D


NEW QUESTION # 95
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 Runs 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 Tasks 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: C

Explanation:
The Tasks tab in the Jobs UI shows the list of tasks that are part of a job, and allows the user to view the details of each task, such as the notebook path, the cluster configuration, the run status, and the duration. By clicking on the active run of a task, the user can access the Spark UI, the notebook output, and the logs of the task. These can help the user to identify the performance bottlenecks and errors in the task. The Runs tab in the Jobs UI only shows the summary of the job runs, such as the start time, the end time, the trigger, and the status. It does not provide the details of the individual tasks within a job run. Reference: Jobs UI, Monitor running jobs with a Job Run dashboard, How to optimize jobs performance


NEW QUESTION # 96
Which of the following data lakehouse features results in improved data quality over a traditional data lake?

  • A. A data lakehouse enables machine learning and artificial Intelligence workloads.
  • B. A data lakehouse provides storage solutions for structured and unstructured data.
  • C. A data lakehouse supports ACID-compliant transactions.
  • D. A data lakehouse allows the use of SQL queries to examine data.
  • E. A data lakehouse stores data in open formats.

Answer: D


NEW QUESTION # 97
......

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