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

Section	Weight	Objectives
Topic 1: Monitoring and Troubleshooting	16%	- Monitoring - Performance Optimization - Troubleshooting
Topic 2: Data Modeling and Storage	20%	- Data Modeling - File Formats - Storage Optimization
Topic 3: Data Quality and Governance	12%	- Governance - Data Lineage - Data Quality
Topic 4: Databricks Lakehouse Platform	24%	- Delta Lake - Unity Catalog - Data Management - Lakehouse Architecture
Topic 5: Data Processing	28%	- Data Transformation - Spark SQL - Structured Streaming - ETL Pipelines

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

A. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
B. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.
C. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
D. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
E. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.

Answer: B

Explanation:
This is the correct answer because it describes how Delta Lake can help to avoid data loss of this nature in the future. By ingesting all raw data and metadata from Kafka to a bronze Delta table, Delta Lake creates a permanent, replayable history of the data state that can be used for recovery or reprocessing in case of errors or omissions in downstream applications or pipelines.
Delta Lake also supports schema evolution, which allows adding new columns to existing tables without affecting existing queries or pipelines. Therefore, if a critical field was omitted from an application that writes its Kafka source to Delta Lake, it can be easily added later and the data can be reprocessed from the bronze table without losing any information.

NEW QUESTION # 61
A platform team lead is responsible for automating the individual teams attribution towards SQL Warehouse usage. The requirement is to identify the SQL warehouse usage at the individual user's level and generate a daily report to be shared with an executive team that includes leaders from all business units. How should the platform lead generate an automated report that can be shared daily?

A. Use the system tables to capture the audit and billing usage data and share the queries with the executive team. This enables the executives to execute the query and see the latest results any time.
B. Restrict users from running any SQL query unless they provide all the query details so that the attribution can be calculated and shared with the executive team.
C. Use the system tables to capture the audit and billing usage data and create a dashboard with daily refresh schedules and shared with the executive team.
D. Let the users run the SQL query and then directly report the usage to the executives. The ownership of the SQL warehouse usage will be with the individual teams.

Answer: C

Explanation:
System tables provide authoritative audit and billing data needed for per-user SQL Warehouse attribution. Creating a dashboard with a scheduled daily refresh automates report generation and ensures executives receive consistent, up-to-date insights without needing to run queries themselves.

NEW QUESTION # 62
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?

A. def new_records():
B. return spark.read.option("readChangeFeed", "true").table ("bronze")
C. return spark.readStream.load("bronze")
D.
E. return spark.readStream.table("bronze")

Answer: D

Explanation:
https://docs.databricks.com/en/delta/delta-change-data-feed.html
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NEW QUESTION # 63
A data engineer inherits a Delta table with historical partitions by country that are badly skewed.
Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve. Which two actions should the data engineer take? (Choose two.)

A. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
B. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
C. Periodically run OPTIMIZE table_name.
D. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
E. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.

Answer: C,D

Explanation:
Liquid Clustering replaces traditional partitioning and ZORDER optimization by automatically organizing data according to clustering keys. It supports evolving clustering strategies without requiring a full table rewrite. To maintain cluster balance and improve performance, the OPTIMIZE command should be run periodically. OPTIMIZE groups data files by clustering keys and helps reduce small file overhead.

NEW QUESTION # 64
A data engineering team needs to create a SQL Alert that monitors data quality across multiple columns in their customer table. They want to trigger an alert when both the percentage of customers with missing email addresses exceeds 15% AND the percentage of customers with invalid phone number formats exceeds 10%. Which SQL query pattern is appropriate for implementing this multi-column alert condition?

A. SELECT email_null_pct, phone_invalid_pct FROM (SELECT (COUNT(CASE WHEN email IS NULL THEN 1 END) *
100.0/COUNT (*)) as email_null_pct, (COUNT(CASE WHEN phone NOT RLIKE ''[0-9-+()\\s]+$' THEN 1 END)*
100.0/COUNT (*)) as phone_invalid_pct FROM customers)
B. SELECT COUNT (*) FROM customers WHERE email IS NULL OR phone_format_invalid = true
C. SELECT email, phone FROM customers WHERE email IS NULL AND phone NOT RLIKE ''[0-9-
+()\\s]+$'
D. SELECT CASE WHEN email_null_pct >15 AND phone_invalid_pct> 10 THEN 1 ELSE 0 END FROM (SELECT (COUNT (CASE WHEN email IS NULL THEN 1 END) * 100.0 / COUNT (*)) as phone_invalid_pct FROM customers) metrics

Answer: A

Explanation:
This pattern computes independent percentage metrics for each data quality condition in a single aggregated query. By calculating the percentage of missing emails and invalid phone formats as separate columns, it enables the SQL Alert to evaluate a compound condition where both thresholds must be exceeded before triggering.

NEW QUESTION # 65
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