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Passing the Databricks Certified Professional Data Engineer exam can be a significant achievement for individuals pursuing a career in big data and cloud computing. Databricks Certified Professional Data Engineer Exam certification demonstrates that the candidate has a deep understanding of Apache Spark and can apply that knowledge to design and implement scalable big data solutions. Additionally, this certification is recognized by many companies in the industry and can improve the candidate's chances of obtaining a high-paying job.

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Databricks Certified Professional Data Engineer exam is a hands-on exam that requires the candidate to complete a set of tasks using Databricks. Databricks-Certified-Professional-Data-Engineer exam evaluates the candidate's ability to design and implement data pipelines, work with data sources and sinks, and perform transformations using Databricks. Databricks-Certified-Professional-Data-Engineer Exam also tests the candidate's ability to optimize and tune data pipelines for performance and reliability.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q212-Q217):

NEW QUESTION # 212
Unity catalog helps you manage the below resources in Databricks at account level

A. Tables
B. All of the above
C. ML Models
D. Dashboards
E. Meta Stores and Catalogs

Answer: B

Explanation:
Explanation
The answer is all of the above.
Unity Catalog is a unified governance solution for all data and AI assets including files, tables, machine learning models, and dashboards in your lakehouse on any cloud.

NEW QUESTION # 213
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.

Which solution would improve the performance?

Answer: B

Explanation:
When joining a stream of advertisement impressions with a stream of user clicks, you want to minimize the state that you need to maintain for the join. Option A suggests using a left outer join with the condition that clickTime == impressionTime, which is suitable for correlating events that occur at the exact same time.
However, in a real-world scenario, you would likely need some leeway to account for the delay between an impression and a possible click. It's important to design the join condition and the window of time considered to optimize performance while still capturing the relevant user interactions. In this case, having the watermark can help with state management and avoid state growing unbounded by discarding old state data that's unlikely to match with new data.

NEW QUESTION # 214
Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table") \
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?

A. The filter condition involves a data type excluded from data skipping support.
B. The event_date column is outside the table's partitioning and Z-ordering scheme.
C. The Delta table lacks optimization that enables dynamic file pruning.
D. The filter is executed only after the full data scan, preventing data skipping.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Delta Lake's data skipping and file pruning optimizations rely on metadata about columns used in partitioning or Z-ordering. If a filter column (e.g., event_date) is not included in the partition or Z-ordering keys, Spark cannot effectively prune files at query time, resulting in full table scans. The Databricks optimization guide states that "File pruning and data skipping are most effective when queries filter on partition or Z-order columns." This explains why the filter was applied but had no impact on the amount of data read. Options A and B are incorrect because Delta automatically applies file pruning when possible; D is less likely, as date columns are fully supported for skipping.

NEW QUESTION # 215
What is the main difference between the silver layer and the gold layer in medalion architecture?

A. Data quality checks are applied in gold
B. God is a copy of silver data
C. Silver is a copy of bronze data
D. Silver may contain aggregated data
E. Gold may contain aggregated data

Answer: E

Explanation:
Explanation
Medallion Architecture - Databricks
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
A diagram of a house Description automatically generated with low confidence

NEW QUESTION # 216
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.
Options:

A. 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
B. 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
C. 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
D. 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

Answer: D

Explanation:
The key requirement is to detect when the daily average of percent_null is greater than 5% for three consecutive days.
Option A only checks the last 24 hours, not consecutive days. It would trigger too frequently and cause spam.
Option C calculates an average across all records in the last 3 days, but this could be skewed by one high or low day - it does not ensure consecutive daily violations.
Option D simply counts days where the threshold was exceeded, but it does not guarantee that those days were consecutive. This could incorrectly trigger on non-adjacent violations.
Option B is correct:
It aggregates hourly values into daily averages.
It checks that the last 3 consecutive days all had averages above 5%.
It avoids redundant alerts by using Notification Frequency: Just once.
This matches Databricks Lakehouse Monitoring best practices, where SQL alerts should be designed to aggregate metrics to the correct granularity (daily here) and ensure consecutive threshold violations before triggering.
Reference (Databricks Lakehouse Monitoring, SQL Alerts Best Practices):
Use DATE_TRUNC to compute metrics at the correct time granularity.
To detect consecutive-day issues, filter the last N daily aggregates and check conditions across all rows.
Always configure alerts with controlled notification frequency to prevent alert fatigue.

NEW QUESTION # 217
......

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