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

Section	Objectives
Topic 1: Data Modeling and Transformation	- Performance optimization techniques - Spark SQL transformations - Dimensional modeling concepts
Topic 2: Data Ingestion and Processing	- ETL pipeline design patterns - Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader
Topic 3: Delta Lake and Data Management	- Schema evolution and enforcement - Delta Lake transactions and ACID properties - Time travel and versioning
Topic 4: Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold)
Topic 5: Production Pipelines and Orchestration	- Error handling and recovery strategies - Databricks Workflows - Job scheduling and monitoring

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q152-Q157):

NEW QUESTION # 152
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When the workspace is being configured, make sure that external cloud object storage has been mounted.
B. Whenever a table is being created, make sure that the location keyword is used.
C. When tables are created, make sure that the external keyword is used in the create table statement.
D. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
E. Whenever a database is being created, make sure that the location keyword is used Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Answer: B

Explanation:
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it.

NEW QUESTION # 153
A data engineer is reviewing the PySpark code to copy a part of the production dataset to the sandbox environment, and needs to be sure that no PII(Personally Identifiable Information) data is being copied. After checking the sales table, the data engineer notices that it has user emails as the only PII data included as well as being the only column to identify the user.
from pyspark.sql import functions as F

Which anonymised code should be used to achieve the required outcome?

A. df.withColumn ("hashed_email", sha2 ("user_email"))
B. df.withColumn ("user_email", F.regexp_replace ("user_eamail", "@*", "@anonymized.com"))
C. df.withColumn ("user_emai", F.expr("uuid()"))
D. df.withColumn ("user_email", F.sha2 ("user_email"))

Answer: D

Explanation:
Hashing the email column replaces the original PII with a deterministic, irreversible value while preserving its role as a unique identifier. This ensures no actual email addresses are copied to the sandbox environment, while still allowing consistent joins or user-level analysis if needed.

NEW QUESTION # 154
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. Rows are marked as deleted in metadata, not in files.
D. Delta automatically removes all cancelled orders permanently.

Answer: C

Explanation:
Deletion vectors (DVs) in Delta Lake optimize delete operations by marking deleted rows logically in metadata rather than rewriting Parquet files. When a DELETE statement is executed, affected rows are tracked by DVs in the transaction log. The data remains in the underlying files but is filtered out during query reads. This improves performance for frequent deletes and updates since file rewrites are deferred. Physical data removal only occurs when a VACUUM command is later executed. The Databricks documentation confirms: "With deletion vectors, deleted rows are marked in metadata and skipped at read time, avoiding file rewrites." Thus, rows are marked as deleted in metadata--not in files.

NEW QUESTION # 155
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. 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.
B. 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.
C. 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.
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: B

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 # 156
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

A. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
B. The source query failed to update properly for three consecutive minutes and then restarted
C. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
D. The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
E. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query

Answer: E

Explanation:
This is the correct answer because the query is using a GROUP BY clause on the sensor_id column, which means it will calculate the mean temperature for each sensor separately. The alert will trigger when the mean temperature for any sensor is greater than 120, which means at least one sensor had an average temperature above 120 for three consecutive minutes. The alert will stop when the mean temperature for all sensors drops below 120.

NEW QUESTION # 157
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