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Databricks Databricks-Certified-Data-Engineer-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Data Engineer Associate Exam
Exam Number:	Databricks-Certified-Data-Engineer-Associate
Passing Score:	70%
Related Certifications:	Databricks Certified Data Analyst Associate
Exam Format:	Multiple Choice, Multiple Select
Real Exam Qty:	60
Exam Duration:	90 minutes
Available Languages:	English
Certificate Validity Period:	2 years
Exam Price:	$200 USD
Sample Questions:	Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions
Exam Way:	Online proctored or in-person testing center
Pre Condition:	Recommended: 6+ months of experience with Databricks and Apache Spark
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-associate

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To become a Databricks Certified Data Engineer Associate, candidates must pass a rigorous exam that tests their knowledge of Databricks architecture, data modeling, data processing, and data integration. Databricks-Certified-Data-Engineer-Associate Exam consists of 60 multiple-choice questions and must be completed within 90 minutes. Candidates must score at least 70% to pass the exam and earn the certification.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q181-Q186):

NEW QUESTION # 181
A data engineer needs to enforce row-level security on main.secure.events(region STRING, event_id STRING). Members of the account group all_regions need to see all rows. All other users need to see only rows where region = 'EU'. Which SQL sequence satisfies the requirement?

Answer: D

Explanation:
The Boolean UDF must be created first and then assigned to the table using SET ROW FILTER ... ON. It returns TRUE for every row when the user belongs to all_regions; otherwise, it returns TRUE only for EU rows.

NEW QUESTION # 182
A data engineer must prevent rows with a null order_id from ever being written to a Delta table, causing the write to fail if violated.
Which command enforces this?

A. CREATE TABLE orders (order_id INT COMMENT 'required')
B. ALTER TABLE orders ALTER COLUMN order_id DROP NOT NULL
C. ALTER TABLE orders SET TBLPROPERTIES ('delta.enforceNotNull' = 'true')
D. ALTER TABLE orders ADD CONSTRAINT id_nn CHECK (order_id IS NOT NULL)

Answer: D

Explanation:
Delta Lake supports CHECK constraints that are validated on every write; any transaction inserting a violating row fails with a constraint violation error. Existing data is validated when the constraint is added, so the command fails if current rows already violate it.

NEW QUESTION # 183
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 without notifications.
C. They can set up an Alert with a new webhook alert destination.
D. They can set up an Alert with a custom template.
E. They can set up an Alert with a new email alert destination.

Answer: C

NEW QUESTION # 184
A data engineer is tasked with cleaning a bronze table. The requirement is to eliminate rows where either the customer_email or the customer_phone field is null. This data cleaning must be performed in a single operation using a single method call. Which PySpark approach supports filtering multiple columns for nulls in one call?

A. df.where('customer_email IS NOT NULL').where('customer_phone IS NOT NULL')
B. df.dropna(subset=['customer_email', 'customer_phone'])
C. df.na.drop(how='all')
D. df.filter(col('customer_email').isNotNull() & col('customer_phone').isNotNull())

Answer: B

Explanation:
df.dropna(subset=['customer_email', 'customer_phone']) uses one method call and, by default, removes rows where either specified column is null.

NEW QUESTION # 185
A company has a strict 15-minute Service Level Agreement (SLA) for updating its currency exchange dashboard. The source data arrives in small increments every few minutes. The data engineering team needs a triggering strategy for their Lakeflow Job that keeps end-to-end latency within the SLA while minimizing compute cost and DBU consumption. Which triggering strategy for the Lakeflow Job is the recommended way to meet this 15-minute SLA?

A. Use a Continuous trigger to ensure data is processed as soon as it arrives.
B. Use a Scheduled trigger set to run every 12 minutes, with the streaming task configured to use Trigger.AvailableNow.
C. Use a Scheduled trigger set to run every 1 minute to ensure the SLA is never at risk.
D. Use a File Arrival trigger configured to fire every time a single JSON file lands in the bucket.

Answer: B

Explanation:
A scheduled Trigger.AvailableNow workload is the most cost-efficient pattern when minute-level latency is acceptable. Running every 12 minutes provides a buffer within the 15-minute SLA while allowing compute to stop after processing all available data. Continuous or per-file execution would consume more compute.

NEW QUESTION # 186
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