100% Pass 2026 Databricks Pass-Sure Certified-Data-Engineer-Professional: New Databricks Certified Data Engineer Professional Practice Questions

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

Section	Objectives
Cost & Performance Optimisation	- Cost Optimization 1. Understand how Unity Catalog managed tables reduce operational overhead - Query Performance 1. Use Query Profile to identify performance bottlenecks 2. Identify inefficient joins and excessive data shuffling - Delta Optimization 1. Understand deletion vectors and liquid clustering 2. Use Change Data Feed to address streaming table limitations and improve latency 3. Apply data skipping and file pruning techniques
Data Modelling	- Dimensional Modelling 1. Design dimensional models for analytical workloads - Scalable Data Models 1. Design and implement scalable data models using Delta Lake 2. Optimize data layout using Liquid Clustering 3. Understand Liquid Clustering versus partitioning and Z-Ordering
Data Transformation, Cleansing, and Quality	- Advanced Data Transformation 1. Write efficient Spark SQL and PySpark transformations 2. Apply window functions, joins, and aggregations to large datasets - Data Quality 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader 2. Develop data quarantining processes for invalid data
Data Sharing and Federation	- Delta Sharing 1. Share live Lakehouse data with external computing platforms 2. Configure sharing with external platforms using the open sharing protocol 3. Configure Databricks-to-Databricks Sharing - Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance
Developing Code for Data Processing using Python and SQL	- Building and Testing ETL Pipelines 1. Develop unit and integration tests for data processing code 2. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 3. Configure environments, dependencies, memory, and retry behavior 4. Use APPLY CHANGES APIs for change data capture 5. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 6. Compare streaming tables and materialized views 7. Use control flow operators in pipeline components 8. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader - Using Python and Tools for Development 1. Manage and troubleshoot third-party library installations and dependencies 2. Design and implement scalable Python project structures optimized for Databricks Asset Bundles 3. Develop User-Defined Functions using Pandas/Python UDFs
Ensuring Data Security and Compliance	- Data Security 1. Apply anonymization and pseudonymization techniques 2. Use row filters and column masks for sensitive data 3. Use ACLs to secure workspace objects and enforce least privilege - Compliance 1. Develop data purging solutions according to data retention policies 2. Implement pipelines that detect and mask personally identifiable information
Monitoring and Alerting	- Monitoring 1. Use system tables for resource, cost, audit, and workload monitoring 2. Use Databricks REST APIs and CLI for monitoring jobs and pipelines 3. Use Lakeflow Spark Declarative Pipelines event logs for monitoring 4. Use Query Profiler and Spark UI to monitor workloads - Alerting 1. Configure Lakeflow Jobs notifications for job status and performance issues 2. Use SQL Alerts for data quality monitoring
Data Governance	- Metadata and Discoverability 1. Create and maintain descriptions and metadata for enterprise data - Unity Catalog Permissions 1. Understand the Unity Catalog permission inheritance model
Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Build append-only pipelines for batch and streaming data using Delta 2. Ingest data from message buses and cloud storage 3. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data
Debugging and Deploying	- Deploying CI/CD 1. Build and deploy Databricks resources using Databricks Asset Bundles 2. Integrate Git-based CI/CD workflows using Databricks Git Folders - Debugging and Troubleshooting 1. Analyze errors and remediate failed job runs 2. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics 3. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging

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Pass-Sure Databricks - Certified-Data-Engineer-Professional - New Databricks Certified Data Engineer Professional Practice Questions

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Databricks Certified Data Engineer Professional Sample Questions (Q33-Q38):

NEW QUESTION # 33
Which statement regarding stream-static joins and static Delta tables is correct?

A. The checkpoint directory will be used to track state information for the unique keys present in the join.
B. The checkpoint directory will be used to track updates to the static Delta table.
C. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
D. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
E. Stream-static joins cannot use static Delta tables because of consistency issues.

Answer: D

Explanation:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch.

NEW QUESTION # 34
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A. preds.write.format("delta").save("/preds/churn_preds")
B.
C. preds.write.mode("append").saveAsTable("churn_preds")
D.
E.

Answer: C

NEW QUESTION # 35
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A. - Total VMs: 16
- 25 GB per Executor
- 10 Cores / Executor
B. - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor
C. - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
D. - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor
E. - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor

Answer: A

NEW QUESTION # 36
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
B. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
C. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
D. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
E. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.

Answer: E

Explanation:
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity.
Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs.

NEW QUESTION # 37
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

A. In the Executor's log file, by gripping for "predicate push-down"
B. In the Query Detail screen, by interpreting the Physical Plan
C. In the Delta Lake transaction log. by noting the column statistics
D. In the Storage Detail screen, by noting which RDDs are not stored on disk
E. In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column

Answer: B

Explanation:
This is the correct answer because it is where in the Spark UI one can diagnose a performance problem induced by not leveraging predicate push-down. Predicate push-down is an optimization technique that allows filtering data at the source before loading it into memory or processing it further. This can improve performance and reduce I/O costs by avoiding reading unnecessary data. To leverage predicate push-down, one should use supported data sources and formats, such as Delta Lake, Parquet, or JDBC, and use filter expressions that can be pushed down to the source. To diagnose a performance problem induced by not leveraging predicate push-down, one can use the Spark UI to access the Query Detail screen, which shows information about a SQL query executed on a Spark cluster. The Query Detail screen includes the Physical Plan, which is the actual plan executed by Spark to perform the query. The Physical Plan shows the physical operators used by Spark, such as Scan, Filter, Project, or Aggregate, and their input and output statistics, such as rows and bytes. By interpreting the Physical Plan, one can see if the filter expressions are pushed down to the source or not, and how much data is read or processed by each operator.

NEW QUESTION # 38
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