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

Section	Weight	Objectives
Topic 1: Debugging and Deploying	10%	- Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors - CI/CD and DevOps practices
Topic 2: Data Governance	7%	- Unity Catalog management - Policy enforcement - Data lineage and metadata tracking
Topic 3: Data Transformation, Cleansing, and Quality	10%	- Standardization and normalization - Handling missing or inconsistent data - Data validation and quality checks
Topic 4: Ensuring Data Security and Compliance	10%	- Access control and permissions - Data encryption and masking - Compliance standards implementation
Topic 5: Developing Code for Data Processing using Python and SQL	22%	- Integration with Databricks APIs and tools - Data transformation and aggregation - Batch and incremental processing logic
Topic 6: Cost & Performance Optimisation	13%	- Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching - Cluster configuration and scaling
Topic 7: Data Sharing and Federation	5%	- Unity Catalog data sharing - Cross-workspace and cross-cloud access
Topic 8: Monitoring and Alerting	10%	- Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications
Topic 9: Data Modelling	6%	- Schema design and management - Delta Lake table design - Medallion Architecture implementation
Topic 10: Data Ingestion & Acquisition	7%	- Schema inference and evolution - Auto Loader and streaming ingestion - Connecting to diverse data sources

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q167-Q172):

NEW QUESTION # 167
A data engineer is designing a system to process batch patient encounter data stored in an S3 bucket, creating a Delta table (patient_encounters) with columns encounter_id, patient_id, encounter_date, diagnosis_code, and treatment_cost. The table is queried frequently by patient_id and encounter_date, requiring fast performance. Fine-grained access controls must be enforced. The engineer wants to minimize maintenance and boost performance.
How should the data engineer create the patient_encounters table?

A. Create a managed table in Hive Metastore. Configure Hive Metastore permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.
B. Create an external table in Unity Catalog, specifying an S3 location for the data files. Enable predictive optimization through table properties, and configure Unity Catalog permissions for access controls.
C. Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, schedule jobs to run OPTIMIZE and VACUUM commands daily to achieve best performance.
D. Create a managed table in Unity Catalog . Configure Unity Catalog permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.

Answer: D

Explanation:
Databricks documentation specifies that Unity Catalog managed tables are the preferred choice for secure, low-maintenance Delta Lake architectures. Managed tables provide full lifecycle management, including metadata, file storage, and access control integration with Unity Catalog. Fine-grained permissions can be enforced at the column and row level through built-in Unity Catalog governance.
Additionally, Predictive Optimization (Auto Optimize + Auto Compaction) automatically manages file sizes, metadata pruning, and layout optimization, eliminating the need for manual maintenance such as scheduling OPTIMIZE or VACUUM.
External tables (A) require manual path management, and Hive Metastore tables (D) do not support Unity Catalog access policies. Therefore, creating a managed Unity Catalog table with predictive optimization provides both the security and performance benefits needed, making B the correct solution.

NEW QUESTION # 168
You noticed that colleague is manually copying the notebook with _bkp to store the previous ver-sions, which of the following feature would you recommend instead.

A. Databricks notebooks should be copied to a local machine and setup source control locally to version the notebooks
B. Databricks notebook can be exported as HTML and imported at a later time
C. Databricks notebooks can be exported into dbc archive files and stored in data lake
D. Databricks notebooks support change tracking and versioning

Answer: D

Explanation:
Explanation
Answer is Databricks notebooks support automatic change tracking and versioning.
When you are editing the notebook on the right side check version history to view all the changes, every change you are making is captured and saved.

NEW QUESTION # 169
Below table temp_data has one column called raw contains JSON data that records temperature for every four hours in the day for the city of Chicago, you are asked to calculate the maximum temperature that was ever recorded for 12:00 PM hour across all the days. Parse the JSON data and use the necessary array function to calculate the max temp.
Table: temp_date
Column: raw
Datatype: string

Expected output: 58

A. 1.select max(raw.chicago.temp[3]) from temp_data
B. 1.select array_max(from_json(raw['chicago'].temp[3],'array<int>')) from temp_data
C. 1.select max(from_json(raw:chicago[3].temp[3],'array<int>')) from temp_data
D. 1.select array_max(from_json(raw:chicago[*].temp[3],'array<int>')) from temp_data
E. 1.select array_max(raw.chicago[*].temp[3]) from temp_data

Answer: D

Explanation:
Explanation
Note: This is a difficult question, more likely you may see easier questions similar to this but the more you are prepared for the exam easier it is to pass the exam.
Use this below link to look for more examples, this will definitely help you,
https://docs.databricks.com/optimizations/semi-structured.html
Here is the solution, step by step
Text Description automatically generated

Use this below link to look for more examples, this will definitely help you,
https://docs.databricks.com/optimizations/semi-structured.html
If you want to try this solution use below DDL,
1.create or replace table temp_data
2. as select ' {
3. "chicago":[
4.{"date":"01-01-2021",
5."temp":[25,28,45,56,39,25]
6.},
7.{"date":"01-02-2021",
8."temp":[25,28,49,54,38,25]
9.},
10.{"date":"01-03-2021",
11."temp":[25,28,49,58,38,25]
12. }]
13. }
14. ' as raw
15.
16.select array_max(from_json(raw:chicago[*].temp[3],'array<int>')) from temp_data
17.

NEW QUESTION # 170
A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production.
How can the data engineer run unit tests against function that work with data in production?

A. Define units test and functions within the same notebook
B. Define and unit test functions using Files in Repos
C. Run unit tests against non-production data that closely mirrors production
D. Define and import unit test functions from a separate Databricks notebook

Answer: C

Explanation:
The best practice for running unit tests on functions that interact with data is to use a dataset that closely mirrors the production data. This approach allows data engineers to validate the logic of their functions without the risk of affecting the actual production data. It's important to have a representative sample of production data to catch edge cases and ensure the functions will work correctly when used in a production environment.
References:
* Databricks Documentation on Testing: Testing and Validation of Data and Notebooks

NEW QUESTION # 171
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in abronzetable created with the propertydelta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
B. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
C. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
D. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
E. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

Answer: B

Explanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

NEW QUESTION # 172
......

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