Databricks-Certified-Data-Engineer-Associate Valid Exam Notes, Databricks-Certified-Data-Engineer-Associate Guide Torrent

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The Databricks Databricks-Certified-Data-Engineer-Associate exam consists of 60 multiple-choice questions and has a duration of 120 minutes. The topics covered in the exam include Databricks architecture, data ingestion, data processing, data transformation, data modeling, and data warehousing. Databricks-Certified-Data-Engineer-Associate exam is intended for data engineers, data architects, data analysts, and data scientists who have experience in working with Databricks for data engineering tasks. By obtaining this certification, candidates can demonstrate their expertise in Databricks and enhance their career prospects in the field of data engineering.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
A data engineer needs to apply custom logic to identify employees with more than 5 years of experience in array column employees in table stores. The custom logic should create a new column exp_employees that is an array of all of the employees with more than 5 years of experience for each row. In order to apply this custom logic at scale, the data engineer wants to use the FILTER higher-order function.
Which of the following code blocks successfully completes this task?

A. Option A
B. Option E
C. Option C
D. Option B
E. Option D

Answer: A

Explanation:
Option A is the correct answer because it uses the FILTER higher-order function correctly to filter out employees with more than 5 years of experience from the array column "employees". It applies a lambda function i -> i.years_exp > 5 that checks if the years of experience of each employee in the array is greater than 5. If this condition is met, the employee is included in the new array column "exp_employees".
References: The use of higher-order functions like FILTER can be referenced from Databricks documentation on Higher-Order Functions.

NEW QUESTION # 74
In which of the following file formats is data from Delta Lake tables primarily stored?

A. JSON
B. Parquet
C. CSV
D. A proprietary, optimized format specific to Databricks
E. Delta

Answer: B

Explanation:
Explanation
https://docs.delta.io/latest/delta-faq.html

NEW QUESTION # 75
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?

A. They can turn on the Serverless feature for the SQL endpoint.
B. They can increase the cluster size of the SQL endpoint.
C. They can increase the maximum bound of the SQL endpoint's scaling range
D. They can turn on the Auto Stop feature for the SQL endpoint.
E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized."

Answer: B

Explanation:
Explanation
https://www.databricks.com/blog/2022/03/10/top-5-databricks-performance-tips.html

NEW QUESTION # 76
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

Answer: A

Explanation:
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html

NEW QUESTION # 77
Which of the following benefits is provided by the array functions from Spark SQL?

A. An ability to work with data in a variety of types at once
B. An ability to work with complex, nested data ingested from JSON files
C. An ability to work with time-related data in specified intervals
D. An ability to work with data within certain partitions and windows
E. An ability to work with an array of tables for procedural automation

Answer: B

Explanation:
The array functions from Spark SQL are a subset of the collection functions that operate on array columns1. They provide an ability to work with complex, nested data ingested from JSON files or other sources2. For example, the explode function can be used to transform an array column into multiple rows, one for each element in the array3. The array_contains function can be used to check if a value is present in an array column4. The array_join function can be used to concatenate all elements of an array column with a delimiter. These functions can be useful for processing JSON data that may have nested arrays or objects. References: 1: Spark SQL, Built-in Functions - Apache Spark 2: Spark SQL Array Functions Complete List - Spark By Examples 3: Spark SQL Array Functions - Syntax and Examples - DWgeek.com 4: Spark SQL, Built-in Functions - Apache Spark : Spark SQL, Built-in Functions - Apache Spark : [Working with Nested Data Using Higher Order Functions in SQL on Databricks - The Databricks Blog]

NEW QUESTION # 78
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