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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q142-Q147):

NEW QUESTION # 142
Predictive Optimization is an automated Databricks service enabled by default for Unity Catalog Managed tables. It helps maintain Delta tables by continuously optimizing them to ensure optimal performance and costs. Which two operations does Predictive Optimization run to maintain the Delta tables? (Choose two.)

A. PARTITION BY
B. ANALYZE
C. OPTIMIZE
D. COMPACT
E. BUCKETING

Answer: B,C

Explanation:
Predictive Optimization automatically runs OPTIMIZE to compact small files and improve data layout, and ANALYZE to collect and refresh table statistics. Together, these operations ensure efficient query planning, better data skipping, and optimized performance and cost for Unity Catalog managed Delta tables.

NEW QUESTION # 143
A data engineering team is collaborating on a Databricks project where each team member needs to develop and test code independently before merging changes into the main branch.
They want to avoid accidental overwrites or branch switching issues while ensuring that all work is version- controlled and can be integrated into their CI/CD pipeline.
How should the data engineer achieve collaboration?

A. Team members edit notebooks directly in the workspace's shared folder and periodically copy changes into a Git folder for version control.
B. Each team member creates their own Databricks Git folder, mapped to the same remote Git repository, and works in their own development branch within their personal folder.
C. Team members use the Databricks CLI to clone the Git repository and perform Git operations from a cluster's web terminal.
D. All team members work in the same Databricks Git folder and perform Git operations (pull, push, commit, branch switching) directly in that shared folder.

Answer: B

Explanation:
Using separate Databricks Git folders per user mapped to the same remote repository allows each team member to work independently on their own branch without interfering with others.
This prevents accidental overwrites or branch conflicts while ensuring all changes are version- controlled and easily integrated into CI/CD workflows.

NEW QUESTION # 144
Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

A. C++
B. Julia
C. Scala Datasets
D. pyspsark.ml.feature
E. Regex

Answer: E

Explanation:
Regex, or regular expressions, are a powerful way of matching patterns in text. They can be used Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from to identify key areas of text when parsing Spark Driver log4j output, such as the log level, the timestamp, the thread name, the class name, the method name, and the message. Regex can be applied in various languages and frameworks, such as Scala, Python, Java, Spark SQL, and Databricks notebooks.

NEW QUESTION # 145
A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

A. Yes; Delta Lake supports infinite concurrent writers.
B. No; only one stream can write to a Delta Lake table.
C. No; Delta Lake manages streaming checkpoints in the transaction log.
D. No; each of the streams needs to have its own checkpoint directory.
E. Yes; both of the streams can share a single checkpoint directory.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Answer: D

Explanation:
This is the correct answer because checkpointing is a critical feature of Structured Streaming that provides fault tolerance and recovery in case of failures. Checkpointing stores the current state and progress of a streaming query in a reliable storage system, such as DBFS or S3. Each streaming query must have its own checkpoint directory that is unique and exclusive to that query. If two streaming queries share the same checkpoint directory, they will interfere with each other and cause unexpected errors or data loss.

NEW QUESTION # 146
To identify the top users consuming compute resources, a data engineering team needs to monitor usage within their Databricks workspace for better resource utilization and cost control.
The team decided to use Databricks system tables, available under the System catalog in Unity Catalog, to gain detailed visibility into workspace activity. Which SQL query should the team run from the System catalog to achieve this?

A. SELECT identity_metadata.run_as AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email
ORDER BY total_dbus DESC
LIMIT 10
B. SELECT sku_name,
identity_metadata.created_by AS user_email,
SUM(usage_quantity * usage_unit) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
C. SELECT sku_name,
usage_metadata.run_name AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
D. SELECT sku_name,
identity_metadata.created_by AS user_email,
COUNT(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10

Answer: A

Explanation:
The system.billing.usage table in the Unity Catalog System schema provides detailed usage metrics for each workload in the workspace. The field identity_metadata.run_as identifies the user or service principal under which the job or query executed. Summing usage_quantity provides total DBU (Databricks Unit) consumption per user. According to Databricks documentation, this table is the authoritative source for monitoring workspace cost drivers, showing compute SKU, user, and DBU consumption over time. Grouping by identity_metadata.run_as and summing usage_quantity produces the correct aggregation to determine top users. Other queries use non- existent or incorrect fields (created_by, run_name, or multiplied usage quantities), which do not reflect actual billing metrics.

NEW QUESTION # 147
......

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