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Databricks Certified Data Engineer Associate Exam is designed for data engineers, software developers, and IT professionals who work with data on a regular basis. Databricks Certified Data Engineer Associate Exam certification program is ideal for individuals who want to demonstrate their expertise in designing and building data pipelines, working with big data, and developing data-driven applications using Databricks.

Databricks Certified Data Engineer Associate Exam is a vendor-neutral certification that tests candidates on their understanding of Databricks architecture and features, data ingestion and processing, data transformation and storage, and machine learning. Databricks-Certified-Data-Engineer-Associate Exam also covers important concepts such as performance tuning, security, and troubleshooting in Databricks. Passing the exam demonstrates that a candidate has the skills, knowledge, and expertise to work with Databricks to design, build, and maintain advanced data pipelines and solutions.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
A data engineer has left the organization. The data team needs to transfer ownership of the data engineer's Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.
Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?

A. Original data engineer
B. This transfer is not possible
C. Workspace administrator
D. Databricks account representative
E. New lead data engineer

Answer: E

NEW QUESTION # 24
A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.
Which of the following describes why Auto Loader inferred all of the columns to be of the string type?

A. There was a type mismatch between the specific schema and the inferred schema
B. Auto Loader cannot infer the schema of ingested data
C. All of the fields had at least one null value
D. Auto Loader only works with string data
E. JSON data is a text-based format

Answer: E

Explanation:
Explanation
JSON data is a text-based format that uses strings to represent all values. When Auto Loader infers the schema of JSON data, it assumes that all values are strings. This is because Auto Loader cannot determine the type of a value based on its string representation. https://docs.databricks.com/en/ingestion/auto-loader/schema.html Forexample, the following JSON string represents a value that is logically a boolean: JSON "true" Use code with caution. Learn more However, Auto Loader would infer that the type of this value is string. This is because Auto Loader cannot determine that the value is a boolean based on its string representation. In order to get Auto Loader to infer the correct types for columns, the data engineer can provide type inference or schema hints. Type inference hints can be used to specify the types of specific columns. Schema hints can be used to provide the entire schema of the data. Therefore, the correct answer is B. JSON data is a text-based format.

NEW QUESTION # 25
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

A. CREATE TABLE AS SELECT statements cannot be used on files
B. Parquet files will become Delta tables
C. Parquet files can be partitioned
D. Parquet files have the ability to be optimized
E. Parquet files have a well-defined schema

Answer: E

Explanation:
Option C is the correct answer because Parquet files have a well-defined schema that is embedded within the data itself. This means that the data types and column names of the Parquet files are automatically detected and preserved when creating an external table from them. This also enables the use of SQL and other structured query languages to access and analyze the data. CSV files, on the other hand, do not have a schema embedded in them, and require specifying the schema explicitly or inferring it from the data when creating an external table from them. This can lead to errors or inconsistencies in the data types and column names, and also increase the processing time and complexity.
References: CREATE TABLE AS SELECT, Parquet Files, CSV Files, Parquet vs. CSV

NEW QUESTION # 26
A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project.
Which command can be used to grant full permissions on the database to the new data engineering team?

A. GRANT ALL PRIVILEGES ON TABLE team TO sales;
B. GRANT SELECT ON TABLE sales TO team;
C. grant all privileges on table sales TO team;
D. GRANT SELECT CREATE MODIFY ON TABLE sales TO team;

Answer: C

Explanation:
To grant full privileges on a table such as 'sales' to a group like 'team', the correct SQL command in Databricks is:
GRANT ALL PRIVILEGES ON TABLE sales TO team;
This command assigns all available privileges, including SELECT, INSERT, UPDATE, DELETE, and any other data manipulation or definition actions, to the specified team. This is typically necessary when a team needs full control over a table to manage and manipulate it as part of a project or ongoing maintenance.
Reference:
Databricks documentation on SQL permissions: SQL Permissions in Databricks

NEW QUESTION # 27
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?

A. They can set up an Alert with one-time notifications.
B. They can set up an Alert with a new webhook alert destination.
C. They can set up an Alert without notifications.
D. They can set up an Alert with a new email alert destination.
E. They can set up an Alert with a custom template.

Answer: B

Explanation:
A webhook alert destination is a notification destination that allows Databricks to send HTTP POST requests to a third-party endpoint when an alert is triggered. This enables the data engineer to integrate Databricks alerts with their preferred messaging or collaboration platform, such as Slack, Microsoft Teams, or PagerDuty.
To set up a webhook alert destination, the data engineer needs to create and configure a webhook connector in their messaging platform, and then add the webhook URL to the Databricks notification destination. After that, the data engineer can create an alert for their Databricks SQL query, and select the webhook alert destination as the notification destination. The alert can be configured with a custom condition, such as when the number of stores with $0 in sales is greater than zero, and a custom message template, such as "Alert:{number_of_stores} stores have $0 in sales". The alert can also be configured with a recurrence interval, such as every hour, to check the query result periodically. When the alert condition is met, the data engineer and their team will receive a notification via the messaging webhook, with the custom message and a link to the Databricks SQL query. The other options are either not suitable for sending notifications via a messaging webhook (A, B, E), or not suitable for sending recurring notifications.
References: Databricks Documentation - Manage notification destinations, Databricks Documentation - Create alerts for Databricks SQL queries, Databricks Documentation - Configure alert conditions and messages.

NEW QUESTION # 28
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Databricks-Certified-Data-Engineer-Associate Study Guides | Reliable Databricks-Certified-Data-Engineer-Associate Exam Cram

Databricks-Certified-Data-Engineer-Associate Sure-Pass Torrent: Databricks Certified Data Engineer Associate Exam & Databricks-Certified-Data-Engineer-Associate Exam Bootcamp & Databricks-Certified-Data-Engineer-Associate Exam Guide

Databricks Certified Data Engineer Associate Exam Sample Questions (Q23-Q28):

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