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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is implementing liquid clustering on a Delta Lale table and needs to understand how it affects data management operations. The table will be updated frequently with new data.
The table is an external table and not managed by Unity Catalog. How does liquid clustering in Delta Lake handle new data that is inserted after the initial table creation?

A) New data is written to a staging area and clustered during scheduled maintenance.
B) New data is automatically clustered during write operations.
C) New data is rejected if it doesn't match the clustering pattern.
D) New data remains unclustered until the next OPTIMIZE operation.

2. A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has 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) await("event_time + `10 minutes'")
B) slidingWindow("event_time", "10 minutes")
C) awaitArrival("event_time", "10 minutes")
D) delayWrite("event_time", "10 minutes")
E) withWatermark("event_time", "10 minutes")

3. A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of information is critical to this decision?

A) Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
B) Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
C) Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.
D) Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.
E) Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.

4. A data engineering team uses Databricks Lakehouse Monitoring to track the percent_null metric for a critical column in their Delta table.
The profile metrics table (prod_catalog.prod_schema.customer_data_profile_metrics) stores hourly percent_null values.
The team wants to:
Trigger an alert when the daily average of percent_null exceeds 5% for
three consecutive days.
Ensure that notifications are not spammed during sustained issues.

A) SELECT SUM(CASE WHEN percent_null > 5 THEN 1 ELSE 0 END) AS violation_days FROM prod_catalog.prod_schema.customer_data_profile_metrics WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '3' DAY Alert Condition: violation_days >= 3 Notification Frequency: Just once
B) WITH daily_avg AS (
SELECT DATE_TRUNC('DAY', window.end) AS day,
AVG(percent_null) AS avg_null
FROM prod_catalog.prod_schema.customer_data_profile_metrics
GROUP BY DATE_TRUNC('DAY', window.end)
)
SELECT day, avg_null
FROM daily_avg
ORDER BY day DESC
LIMIT 3
Alert Condition: ALL avg_null > 5 for the latest 3 rows
Notification Frequency: Just once
C) SELECT AVG(percent_null) AS daily_avg
FROM prod_catalog.prod_schema.customer_data_profile_metrics
WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '3' DAY
Alert Condition: daily_avg > 5
Notification Frequency: Each time alert is evaluated
D) SELECT percent_null
FROM prod_catalog.prod_schema.customer_data_profile_metrics
WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '1' DAY
Alert Condition: percent_null > 5
Notification Frequency: At most every 24 hours

5. A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?

A) Define and import unit test functions from a separate Databricks notebook
B) Define units test and functions within the same notebook
C) Define and unit test functions using Files in Repos
D) Run unit tests against non-production data that closely mirrors production

Solutions: