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

Section	Weight	Objectives
Topic 1: Delta Lake	20-25%	- Delta Lake fundamentals 1. Optimize and Z-order 2. ACID transactions 3. Time travel and data versioning - Delta Lake operations 1. Merge, update, delete operations 2. Delta Live Tables 3. Schema evolution and enforcement
Topic 2: Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Built-in and user-defined functions 2. Spark APIs in Python 3. Performance optimization techniques - Spark DataFrames and Spark SQL 1. Window functions 2. Spark SQL queries and functions 3. DataFrame operations and transformations
Topic 3: Data Ingestion	15-20%	- Batch ingestion methods 1. Spark APIs for ingestion 2. Integration with external systems 3. DBR autoloader - Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals
Topic 4: Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Differences between data lake, data warehouse, and lakehouse 2. Data governance fundamentals 3. Bronze, silver, gold data layers
Topic 5: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Monitoring and alerting 2. Jobs and job scheduling 3. Task dependencies and orchestration

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q173-Q178):

NEW QUESTION # 173
A platform team lead is responsible for automating SQL Warehouse usage attribution across business units. They need to identify warehouse usage at the individual user level and share a daily usage report with an executive team that includes business leaders from multiple departments.
How should the platform lead generate an automated report that can be shared daily?

A. Restrict users from running SQL queries unless they provide query details for attribution tracking.
B. Use system tables to capture audit and billing usage data and share the queries with the executive team for manual execution.
C. Let users run queries normally and have individual teams manually report usage to the executive team.
D. Use system tables to capture audit and billing usage data and create a dashboard with a daily refresh schedule shared with the executive team.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks provides system tables under the system.billing and system.access schemas for auditing and cost attribution across SQL Warehouses. The best practice is to build a dashboard in Databricks SQL using these system tables and configure it with a daily refresh schedule. This ensures automated, up-to-date reporting of compute consumption, user activity, and cost distribution. Sharing this dashboard with executives provides transparency without requiring them to run queries manually. This approach aligns with Databricks' operational guidance on workspace observability and cost governance, making B the correct answer.

NEW QUESTION # 174
While investigating a performance issue, you realized that you have too many small files for a given table, which command are you going to run to fix this issue

A. SHRINK table_name
B. OPTIMIZE table_name
C. MERGE table_name
D. COMPACT table_name
E. VACUUM table_name

Answer: B

Explanation:
Explanation
The answer is OPTIMIZE table_name,
Optimize compacts small parquet files into a bigger file, by default the size of the files are determined based on the table size at the time of OPTIMIZE, the file size can also be set manually or adjusted based on the workload.
https://docs.databricks.com/delta/optimizations/file-mgmt.html
Tune file size based on Table size
To minimize the need for manual tuning, Databricks automatically tunes the file size of Delta tables based on the size of the table. Databricks will use smaller file sizes for smaller tables and larger file sizes for larger tables so that the number of files in the table does not grow too large.
Table Description automatically generated

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NEW QUESTION # 175
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI. Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job. They attempt to transfer
"Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

A. Only workspace administrators can grant "Owner" privileges to a group.
B. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
C. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
D. A user can only transfer job ownership to a group if they are also a member of that group.
E. Other than the default "admins" group, only individual users can be granted privileges on jobs.

Answer: C

Explanation:
The reason why the junior data engineer cannot transfer "Owner" privileges to the "DevOps" group is that Databricks jobs must have exactly one owner, and the owner must be an individual user, not a group. A job cannot have more than one owner, and a job cannot have a group as an owner. The owner of a job is the user who created the job, or the user who was assigned the ownership by another user. The owner of a job has the highest level of permission on the job, and can grant or revoke permissions to other users or groups. However, the owner cannot transfer the ownership to a group, only to another user. Therefore, the junior data engineer's attempt to transfer "Owner" privileges to the "DevOps" group is not possible. References:
* Jobs access control: https://docs.databricks.com/security/access-control/table-acls/index.html
* Job permissions:
https://docs.databricks.com/security/access-control/table-acls/privileges.html#job-permissions

NEW QUESTION # 176
A table named user_ltv is being used to create a view that will be used by data analysts on various teams.
Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A. Only the email and itv columns will be returned; the email column will contain all null values.
B. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
C. Three columns will be returned, but one column will be named "redacted" and contain only null values.
D. The email, age. and ltv columns will be returned with the values in user ltv.
E. The email and ltv columns will be returned with the values in user itv.

Answer: B

Explanation:
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row.
Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CASE expression" section.

NEW QUESTION # 177
How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

A. Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.
B. Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.
C. Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.
D. Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks documentation explains that Lakeflow Declarative Pipelines build upon Structured Streaming but add higher-level orchestration and automation capabilities. They automatically manage dependencies, materialization, and recovery across multi-stage data flows without requiring external orchestration tools such as Airflow or Azure Data Factory. In contrast, Structured Streaming operates at a lower level, where developers must manually handle orchestration, retries, and dependencies between streaming jobs. Both support Delta Lake outputs and schema evolution; however, Lakeflow Declarative Pipelines simplify management by declaratively defining transformations and data quality expectations. Hence, the correct distinction is A - automated orchestration and management in Lakeflow Declarative Pipelines.

NEW QUESTION # 178
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