Databricks Databricks-Certified-Data-Engineer-Professional Value Pack (Frequently Bought Together)

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Precise predication of the real exam

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

Databricks Certified Data Engineer Professional Sample Questions:

1. 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 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) 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
D) 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

2. A company stores account transactions in a Delta Lake table. The company needs to apply frequent account-level correlations (e.g., UPDATE statements) but wants to avoid rewriting entire Parquet files for each change to reduce file churn and improve write performance. Which Delta Lake feature should they enable?

A) Enable automatic file compaction on writes
B) Enable deletion vectors on the Delta table
C) Partition the Delta table by account_id
D) Enable change data feed on the Delta table

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

4. The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

A) Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
B) Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
C) Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.
D) Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.
E) Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.

5. A data engineer has a Delta table orders with deletion vectors enabled. The engineer executes the following command:
DELETE FROM orders WHERE status = 'cancelled';
What should be the behavior of deletion vectors when the command is executed?

A) Rows are marked as deleted both in metadata and in files.
B) Files are physically rewritten without the deleted rows.
C) Delta automatically removes all cancelled orders permanently.
D) Rows are marked as deleted in metadata, not in files.

Solutions: