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Databricks Certified Professional Data Engineer Exam is a certification program designed for data professionals who want to demonstrate their expertise in building, deploying, and maintaining data engineering solutions using Databricks. Databricks-Certified-Professional-Data-Engineer Exam covers a wide range of topics related to data engineering and requires a thorough understanding of Databricks data engineering concepts and techniques. Databricks-Certified-Professional-Data-Engineer exam is challenging and requires the candidate to demonstrate their ability to perform specific tasks using Databricks.

Databricks Certified Professional Data Engineer certification is a valuable credential for data engineers who want to demonstrate their expertise in using the Databricks platform. It provides employers with a way to identify and verify the skills of candidates and employees, and it can help data engineers advance their careers by demonstrating their proficiency in using the Databricks platform to build and maintain scalable and reliable data pipelines.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
A SQL Dashboard was built for the supply chain team to monitor the inventory and product orders, but all of the timestamps displayed on the dashboards are showing in UTC format, so they requested to change the time zone to the location of New York. How would you approach resolving this issue?

A. Change the timestamp on the delta tables to America/New_York format
B. Move the workspace from Central US zone to East US Zone
C. Add SET Timezone = America/New_York on every of the SQL queries in the dashboard.
D. Change the spark configuration of SQL endpoint to format the timestamp to Ameri-ca/New_York
E. Under SQL Admin Console, set the SQL configuration parameter time zone to Ameri-ca/New_York

Answer: E

Explanation:
Explanation
The answer is, Under SQL Admin Console, set the SQL configuration parameter time zone to America/New_York Here are steps you can take this to configure, so the entire dashboard is changed without changing individual queries Configure SQL parameters To configure all warehouses with SQL parameters:
1.Click Settings at the bottom of the sidebar and select SQL Admin Console.
2.Click the SQL Warehouse Settings tab.
3.In the SQL Configuration Parameters textbox, specify one key-value pair per line. Sepa-rate the name of the parameter from its value using a space. For example, to ena-ble ANSI_MODE:
Graphical user interface, text, application Description automatically generated

Similarly, we can add a line in the SQL Configuration parameters
timezone America/New_York
SQL configuration parameters | Databricks on AWS

NEW QUESTION # 71
Newly joined data analyst requested read-only access to tables, assuming you are owner/admin which section of Databricks platform is going to facilitate granting select access to the user

A. User settings
B. Data explorer
C. Azure Databricks control pane IAM
D. Azure RBAC
E. Admin console

Answer: B

Explanation:
Explanation
Anser is Data Explorer
https://docs.databricks.com/sql/user/data/index.html
Data explorer lets you easily explore and manage permissions on databases and tables. Users can view schema details, preview sample data, and see table details and properties. Administrators can view and change owners, and admins and data object owners can grant and revoke permissions.
To open data explorer, click Data in the sidebar.

NEW QUESTION # 72
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFramedf. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFramedfhas the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A. window("event_time", "10 minutes").alias("time")
B. "event_time"
C. lag("event_time", "10 minutes").alias("time")
D. to_interval("event_time", "5 minutes").alias("time")
E. window("event_time", "5 minutes").alias("time")

Answer: E

Explanation:
Explanation
This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1 second. In this case, the window duration is "5 minutes", which means each window will cover a non-overlapping five-minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window. The alias function is used to rename the struct column as "time". Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "WINDOW" section.https://www.databricks.com/blog/2017/05/08/event-time-aggregation-watermarking-apache-sparks-struc

NEW QUESTION # 73
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A. Can Read
B. Can manage
C. Can run
D. Can edit

Answer: A

Explanation:
Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.References: Databricks documentation on access control and permissions for notebooks within the workspace (https://docs.databricks.com/security/access-control/workspace-acl.html).

NEW QUESTION # 74
A data engineer has a Job with multiple tasks that runs nightly. One of the tasks unexpectedly fails during 10
percent of the runs.
Which of the following actions can the data engineer perform to ensure the Job completes each night while
minimizing compute costs?

A. They can institute a retry policy for the entire Job
B. They can utilize a Jobs cluster for each of the tasks in the Job
C. They can institute a retry policy for the task that periodically fails
D. They can set up the Job to run multiple times ensuring that at least one will complete
E. They can observe the task as it runs to try and determine why it is failing

Answer: C

NEW QUESTION # 75
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