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The GAQM Databricks-Certified-Data-Engineer-Associate Certification Exam is an important certification program for data engineers who work with Databricks. Databricks Certified Data Engineer Associate Exam certification demonstrates the candidate's expertise and knowledge in data engineering and is a valuable credential for professionals who want to advance their careers in the field. Candidates can prepare for the exam by taking advantage of various study resources, including online courses, study guides, and practice exams.
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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) certification exam is an excellent opportunity for data professionals to validate their skills in designing, building, and maintaining data pipelines using Databricks. Databricks Certified Data Engineer Associate Exam certification exam covers a wide range of topics and is designed to test the candidate's knowledge of data engineering fundamentals, Databricks architecture, data modeling, and data processing. Passing the certification exam provides an individual with a globally recognized certification that can enhance their career prospects in the data engineering field.
NEW QUESTION # 278
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when it is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?
Answer: A
Explanation:
The Auto Stop feature allows the SQL endpoint to automatically stop after a specified period of inactivity. This can help reduce the cost and resource consumption of the SQL endpoint, as it will only run when it is needed to refresh the dashboard or execute queries. The data engineer can configure the Auto Stop setting for the SQL endpoint from the SQL Endpoints UI, by selecting the desired idle time from the Auto Stop dropdown menu. The default idle time is 120 minutes, but it can be set to as low as 15 minutes or as high as 240 minutes. Alternatively, the data engineer can also use the SQL Endpoints REST API to set the Auto Stop setting programmatically. Reference: SQL Endpoints UI, SQL Endpoints REST API, Refreshing SQL Dashboard
NEW QUESTION # 279
A data engineer only wants to execute the final block of a Python program if the Python variable day_of_week is equal to 1 and the Python variable review_period is True.
Which of the following control flow statements should the data engineer use to begin this conditionally executed code block?
Answer: C
NEW QUESTION # 280
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?
Answer: D
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 # 281
A data engineer is working with two tables. Each of these tables is displayed below in its entirety.
The data engineer runs the following query to join these tables together:
Which of the following will be returned by the above query?
Answer: B
Explanation:
Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition is ON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns:
customer_id, name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.
The use of INNER JOIN can be referenced from Databricks documentation on SQL JOIN or from other sources like W3Schools or GeeksforGeeks.
NEW QUESTION # 282
A Databricks single-task workflow fails at the last task due to an error in a notebook. The data engineer fixes the mistake in the notebook. What should the data engineer do to rerun the workflow?
Answer: D
Explanation:
For a single-task workflow, the correct action is to repair the task, which reruns only the failed task after fixing the error, avoiding unnecessary re-execution of the whole workflow.
NEW QUESTION # 283
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