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

Section	Objectives
Topic 1: Data Sharing and Federation	- Share and federate data 1. Use Delta Sharing to share live data from the Lakehouse with any computing platform 2. Configure Lakehouse Federation with appropriate governance across supported source systems 3. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol
Topic 2: Cost & Performance Optimization	- Optimize cost and performance 1. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning 2. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden 3. Apply Change Data Feed to address streaming table limitations and improve latency 4. Understand Delta optimization techniques such as deletion vectors and liquid clustering 5. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling
Topic 3: Data Governance	- Govern enterprise data 1. Demonstrate understanding of the Unity Catalog permission inheritance model 2. Create and add descriptions and metadata to enterprise data to improve discoverability
Topic 4: Ensuring Data Security and Compliance	- Ensuring Compliance 1. Implement compliant batch and streaming pipelines that detect and mask PII 2. Develop data purging solutions that comply with data retention policies - Applying Data Security Mechanisms 1. Use row filters and column masks to protect sensitive table data 2. Use ACLs to secure workspace objects and enforce the principle of least privilege 3. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization
Topic 5: Data Transformation, Cleansing, and Quality	- Transform and validate data 1. Write efficient Spark SQL and PySpark code for advanced transformations including window functions, joins, and aggregations 2. Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or Auto Loader in classic jobs
Topic 6: Data Modeling	- Design and optimize data models 1. Design dimensional models for analytical workloads with efficient querying and aggregation 2. Design and implement scalable data models using Delta Lake to manage large datasets 3. Identify the benefits of liquid clustering over partitioning and Z-Ordering 4. Simplify data layout decisions and optimize query performance using liquid clustering
Topic 7: Developing Code for Data Processing using Python and SQL	- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 2. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 3. Create pipeline components using control flow operators such as if/else and foreach 4. Explain the advantages and disadvantages of streaming tables compared to materialized views 5. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 6. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 7. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 8. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI - Using Python and Tools for Development 1. Develop User-Defined Functions using Pandas/Python UDF 2. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration 3. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives
Topic 8: Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Create an append-only data pipeline capable of handling both batch and streaming data using Delta 2. Ingest formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, text, and binary data from sources such as message buses and cloud storage
Topic 9: Debugging and Deploying	- Debugging and Troubleshooting 1. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines 2. Analyze errors and remediate failed job runs using job repairs and parameter overrides 3. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors - Deploying CI/CD 1. Build and deploy Databricks resources using Databricks Asset Bundles 2. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment
Topic 10: Monitoring and Alerting	- Alerting 1. Use SQL Alerts to monitor data quality 2. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues - Monitoring 1. Use system tables for observability of resource utilization, cost, auditing, and workloads 2. Use Lakeflow Declarative Pipelines event logs to monitor pipelines 3. Use Query Profile and Spark UI to monitor workloads 4. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines

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Databricks Certified Data Engineer Professional Sample Questions (Q110-Q115):

NEW QUESTION # 110
A data engineer is evaluating tools to build a production-grade data pipeline. The team must process change data from cloud object storage, filter out or isolate invalid records, and ensure the timely delivery of clean data to downstream consumers. The team is small, under tight deadlines, and wants to minimize operational overhead while keeping pipelines auditable and maintainable.
Which approach should the data engineer implement?

A. Use a hybrid approach: Ingest with Auto Loader into Bronze tables, then process using SQL queries in Databricks Workflows to generate cleaned Silver and Gold tables on a schedule.
B. Implement ingestion using Auto Loader with Structured Streaming, and manage invalid data handling and table updates using checkpointing and merge logic.
C. Ingest data directly into Delta tables via Spark jobs, apply data quality filters using UDFs, and use LDP for creating Materialized Views.
D. Use LDP to build declarative pipelines with Streaming Tables and Materialized Views, leveraging built-in support for data expectations and incremental processing.

Answer: D

Explanation:
LDP provides a declarative framework for building production-grade pipelines with minimal operational overhead. Streaming Tables and Materialized Views handle incremental processing automatically, while built-in data expectations allow invalid records to be filtered or isolated in a consistent and auditable way. This approach is well suited for small teams under tight deadlines, as it simplifies maintenance, improves reliability, and ensures timely delivery of clean data to downstream consumers.

NEW QUESTION # 111
A company has a task management system that tracks the most recent status of tasks. The system takes task events as input and processes events in near real-time using Lakeflow Declarative Pipelines. A new task event is ingested into the system when a task is created or the task status is changed. Lakeflow Declarative Pipelines provides a streaming table (tasks_status) for BI users to query.
The table represents the latest status of all tasks and includes 5 columns:
task_id (unique for each task)
task_name
task_owner
task_status
task_event_time
The table enables three properties: deletion vectors, row tracking, and change data feed (CDF).
A data engineer is asked to create a new Lakeflow Declarative Pipeline to enrich the tasks_status table in near real-time by adding one additional column representing task_owner's department, which can be looked up from a static dimension table (employee).
How should this enrichment be implemented?

A. Create a new Lakeflow Declarative Pipeline: use the read() function to read tasks_status table; enrich with employee table; store the result in a materialized view.
B. Create a new Lakeflow Declarative Pipeline: use the readStream() function with the option skipChangeCommits to read the tasks_status table; enrich with the employee table; store the result in a new streaming table.
C. Create a new Lakeflow Declarative Pipeline: use readStream() function with option readChangeFeed to read tasks_status table CDF; enrich with the employee table; create a new streaming table as the result table and use apply_changes() function to process the changes from the enriched CDF.
D. Create a new Lakeflow Declarative Pipeline: use the readStream() function to read tasks_status table; enrich with the employee table; store the result in a new streaming table.

Answer: C

Explanation:
Change Data Feed (CDF) allows downstream consumers to read incremental changes (inserts, updates, deletes) from a Delta table. The documentation explains that when streaming from a Delta table with CDF enabled, developers can use readStream().option("readChangeFeed","true") to capture incremental events. For maintaining a derived table with enrichment logic, the recommended practice is to use apply_changes(), which applies CDC semantics (insert/update/delete) correctly to the target streaming table. By joining with the static employee dimension, enriched rows are generated before being merged into the new streaming target. This ensures correctness, scalability, and minimal latency. Batch reads or skipping commits do not maintain correctness for CDC pipelines.

NEW QUESTION # 112
A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

A. Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.
B. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.
C. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.
D. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
E. The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.

Answer: D

Explanation:
The adjustment that will meet the requirement of processing records in less than 10 seconds is to decrease the trigger interval to 5 seconds. This is because triggering batches more frequently may prevent records from backing up and large batches from causing spill. Spill is a phenomenon where the data in memory exceeds the available capacity and has to be written to disk, which can slow down the processing and increase the execution time. By reducing the trigger interval, the streaming query can process smaller batches of data more quickly and avoid spill. This can also improve the latency and throughput of the streaming job.

NEW QUESTION # 113
Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table")
\
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?

A. The event_date column is outside the table's partitioning and Z-ordering scheme.
B. The filter is executed only after the full data scan, preventing data skipping.
C. The filter condition involves a data type excluded from data skipping support.
D. The Delta table lacks optimization that enables dynamic file pruning.

Answer: A

Explanation:
Delta Lake's data skipping relies on partitioning and clustering (such as Z-ordering) on the filtered columns. If event_date is neither a partition column nor included in the table's clustering strategy, Spark must scan most files because file-level statistics cannot be effectively used to prune irrelevant data.

NEW QUESTION # 114
Which of the following is true of Delta Lake and the Lakehouse?

A. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
B. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
C. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
D. Z-order can only be applied to numeric values stored in Delta Lake tables
E. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.

Answer: A

Explanation:
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan. This can significantly improve the query performance and reduce the I/O cost.

NEW QUESTION # 115
......

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