Databricks-Certified-Data-Engineer-Associate Exam Questions - Databricks Certified Data Engineer Associate Exam Torrent Prep & Databricks-Certified-Data-Engineer-Associate Test Guide

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q138-Q143):

NEW QUESTION # 138
A data engineer wants to inspect the schema of a Spark DataFrame during development to understand column names and data types. Which DataFrame method prints the schema in a tree format?

A. getSchema
B. printSchema
C. describeSchema
D. showSchema

Answer: B

NEW QUESTION # 139
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. org.apache.spark.sql.sqlite
B. sqlite
C. org.apache.spark.sql.jdbc
D. autoloader
E. DELTA

Answer: B

Explanation:
1: In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. Reference:
Create a table using JDBC
JDBC connection string
SQLite JDBC driver

NEW QUESTION # 140
Which of the following approaches should be used to send the Databricks Job owner an email in the case that the Job fails?

A. Setting up an Alert in the Notebook
B. MLflow Model Registry Webhooks
C. There is no way to notify the Job owner in the case of Job failure
D. Manually programming in an alert system in each cell of the Notebook
E. Setting up an Alert in the Job page

Answer: E

Explanation:
To send the Databricks Job owner an email in the case that the Job fails, the best approach is to set up an Alert in the Job page. This way, the Job owner can configure the email address and the notification type for the Job failure event. The other options are either not feasible, not reliable, or not relevant for this task. Manually programming an alert system in each cell of the Notebook is tedious and error-prone. Setting up an Alert in the Notebook is not possible, as Alerts are only available for Jobs and Clusters. There is a way to notify the Job owner in the case of Job failure, so option D is incorrect. MLflow Model Registry Webhooks are used for model lifecycle events, not Job events, so option E is not applicable. Reference:
Add email and system notifications for job events
Alerts
MLflow Model Registry Webhooks

NEW QUESTION # 141
Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?

A. UPDATE my_table WHERE age > 25;
B. DELETE FROM my_table WHERE age > 25;
C. UPDATE my_table WHERE age <= 25;
D. DELETE FROM my_table WHERE age <= 25;
E. SELECT * FROM my_table WHERE age > 25;

Answer: B

Explanation:
The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table. This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate, but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. Reference: Table deletes, updates, and merges - Delta Lake Documentation

NEW QUESTION # 142
A data engineer needs to process SQL queries on a large dataset with fluctuating workloads. The workload requires automatic scaling based on the volume of queries, without the need to manage or provision infrastructure. The solution should be cost-efficient and charge only for the compute resources used during query execution. Which compute option should the data engineer use?

A. Databricks Runtime for ML
B. Databricks SQL Analytics
C. Databricks Jobs
D. Serverless SQL Warehouse

Answer: D

Explanation:
A Serverless SQL Warehouse automatically scales to handle fluctuating workloads, requires no infrastructure management, and charges only for the compute used during query execution, making it cost-efficient for large datasets.

NEW QUESTION # 143
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