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

Section	Objectives
Topic 1: Ensuring Data Security and Compliance	- Data Security 1. Apply anonymization and pseudonymization techniques 2. Use ACLs to secure workspace objects and enforce least privilege 3. Use row filters and column masks for sensitive data - Compliance 1. Implement pipelines that detect and mask personally identifiable information 2. Develop data purging solutions according to data retention policies
Topic 2: 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
Topic 3: 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. Use Change Data Feed to address streaming table limitations and improve latency 2. Understand deletion vectors and liquid clustering 3. Apply data skipping and file pruning techniques
Topic 4: Monitoring and Alerting	- Alerting 1. Use SQL Alerts for data quality monitoring 2. Configure Lakeflow Jobs notifications for job status and performance issues - Monitoring 1. Use Query Profiler and Spark UI to monitor workloads 2. Use Lakeflow Spark Declarative Pipelines event logs for monitoring 3. Use Databricks REST APIs and CLI for monitoring jobs and pipelines 4. Use system tables for resource, cost, audit, and workload monitoring
Topic 5: Developing Code for Data Processing using Python and SQL	- Using Python and Tools for Development 1. Manage and troubleshoot third-party library installations and dependencies 2. Develop User-Defined Functions using Pandas/Python UDFs 3. Design and implement scalable Python project structures optimized for Databricks Asset Bundles - Building and Testing ETL Pipelines 1. Configure environments, dependencies, memory, and retry behavior 2. Use control flow operators in pipeline components 3. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 4. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 5. Use APPLY CHANGES APIs for change data capture 6. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 7. Develop unit and integration tests for data processing code 8. Compare streaming tables and materialized views
Topic 6: Data Transformation, Cleansing, and Quality	- Data Quality 1. Develop data quarantining processes for invalid data 2. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader - Advanced Data Transformation 1. Write efficient Spark SQL and PySpark transformations 2. Apply window functions, joins, and aggregations to large datasets
Topic 7: Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data 2. Ingest data from message buses and cloud storage 3. Build append-only pipelines for batch and streaming data using Delta
Topic 8: Debugging and Deploying	- Debugging and Troubleshooting 1. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging 2. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics 3. Analyze errors and remediate failed job runs - Deploying CI/CD 1. Build and deploy Databricks resources using Databricks Asset Bundles 2. Integrate Git-based CI/CD workflows using Databricks Git Folders
Topic 9: Data Modelling	- Dimensional Modelling 1. Design dimensional models for analytical workloads - Scalable Data Models 1. Optimize data layout using Liquid Clustering 2. Design and implement scalable data models using Delta Lake 3. Understand Liquid Clustering versus partitioning and Z-Ordering
Topic 10: Data Sharing and Federation	- Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance - Delta Sharing 1. Configure Databricks-to-Databricks Sharing 2. Configure sharing with external platforms using the open sharing protocol 3. Share live Lakehouse data with external computing platforms

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Databricks Certified Data Engineer Professional Sample Questions (Q217-Q222):

NEW QUESTION # 217
A data engineer needs to design an efficient pipeline that automatically processes new CSV files as they arrive in S3 storage. Which Databricks feature should the data engineer use to meet these requirements?

A. Traditional batch processing with scheduled Databricks Jobs
B. Streaming from cloud storage using standard Spark readStream with format ("csv") and format ("json")
C. Auto Loader with schema inference and evolution enabled
D. COPY INTO SQL command with parameters to track processed files

Answer: C

Explanation:
Auto Loader is designed to efficiently and incrementally process new files as they arrive in cloud object storage. It provides scalable file discovery, supports schema inference and evolution, and minimizes overhead compared to traditional batch or manual streaming approaches.

NEW QUESTION # 218
A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

A. Merge all changes back to the main branch in the remote Git repository and clone the repo again
B. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
C. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
D. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
E. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.

Answer: E

Explanation:
This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook.

NEW QUESTION # 219
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:

The table daily_store_sales contains all the information needed to update store_sales_summary.
The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

A. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
B. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
C. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
D. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
E. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.

Answer: D

NEW QUESTION # 220
The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A. Cluster creation allowed. "Can Restart" privileges on the required cluster
B. Cluster creation allowed. "Can Attach To" privileges on the required cluster
C. "Can Restart" privileges on the required cluster
D. "Can Manage" privileges on the required cluster
E. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster

Answer: C

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/security/auth-authz/access-control/cluster-acl
https://docs.databricks.com/en/security/auth-authz/access-control/cluster-acl.html

NEW QUESTION # 221
A company stores account transactions in a Delta Lake table. The company needs to apply frequent account-level correlations (e.g., UPDATE statements) but wants to avoid rewriting entire Parquet files for each change to reduce file churn and improve write performance. Which Delta Lake feature should they enable?

A. Enable deletion vectors on the Delta table
B. Enable automatic file compaction on writes
C. Enable change data feed on the Delta table
D. Partition the Delta table by account_id

Answer: A

Explanation:
Deletion vectors allow Delta Lake to track row-level deletes and updates without rewriting entire Parquet files. By recording changes separately from the base files, this feature significantly reduces file churn and improves write performance for workloads with frequent row-level modifications such as account-level updates.

NEW QUESTION # 222
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