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

Section	Objectives
Cost & Performance Optimization	- Optimize cost and performance 1. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling 2. Apply Change Data Feed to address streaming table limitations and improve latency 3. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden 4. Understand Delta optimization techniques such as deletion vectors and liquid clustering 5. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning
Data Sharing and Federation	- Share and federate data 1. Configure Lakehouse Federation with appropriate governance across supported source systems 2. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol 3. Use Delta Sharing to share live data from the Lakehouse with any computing platform
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
Data Transformation, Cleansing, and Quality	- Transform and validate data 1. Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or Auto Loader in classic jobs 2. Write efficient Spark SQL and PySpark code for advanced transformations including window functions, joins, and aggregations
Ensuring Data Security and Compliance	- Applying Data Security Mechanisms 1. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization 2. Use row filters and column masks to protect sensitive table data 3. Use ACLs to secure workspace objects and enforce the principle of least privilege - Ensuring Compliance 1. Develop data purging solutions that comply with data retention policies 2. Implement compliant batch and streaming pipelines that detect and mask PII
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
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
Data Modeling	- Design and optimize data models 1. Design and implement scalable data models using Delta Lake to manage large datasets 2. Simplify data layout decisions and optimize query performance using liquid clustering 3. Identify the benefits of liquid clustering over partitioning and Z-Ordering 4. Design dimensional models for analytical workloads with efficient querying and aggregation
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 Databricks REST APIs and Databricks CLI to monitor jobs and pipelines 3. Use Query Profile and Spark UI to monitor workloads 4. Use Lakeflow Declarative Pipelines event logs to monitor pipelines
Developing Code for Data Processing using Python and SQL	- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 2. Explain the advantages and disadvantages of streaming tables compared to materialized views 3. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 4. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI 5. Create pipeline components using control flow operators such as if/else and foreach 6. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 7. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 8. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader - Using Python and Tools for Development 1. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives 2. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration 3. Develop User-Defined Functions using Pandas/Python UDF

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

NEW QUESTION # 217
A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
B. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
C. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
E. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.

Answer: C

Explanation:
Table is created and data of join will be stored on DBFS and it will be returned on query time.

NEW QUESTION # 218
In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone. A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.
Why are the cloned tables no longer working?

A. Tables created with SHALLOW CLONE are automatically deleted after their default retention threshold of 7 days.
B. The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.
C. Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.
D. Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.
E. The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command

Answer: E

Explanation:
In Delta Lake, a shallow clone creates a new table by copying the metadata of the source table without duplicating the data files. When the vacuum command is run on the source table, it removes old data files that are no longer needed to maintain the transactional log's integrity, potentially including files referenced by the shallow clone's metadata. If these files are purged, the shallow cloned tables will reference non-existent data files, causing them to stop working properly. This highlights the dependency of shallow clones on the source table's data files and the impact of data management operations like vacuum on these clones.

NEW QUESTION # 219
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

A. Troubleshooting is easier since all steps are isolated and tested individually
B. Validates a complete use case of your application
C. Ensures that all steps interact correctly to achieve the desired end result
D. Yields faster deployment and execution times
E. Improves the quality of your data

Answer: A

NEW QUESTION # 220
A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats. After converting a subset of data to Delta Lake, they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join, whereas queries on legacy formats used Sort Merge Joins. The queries reading Delta Lake data also scanned less data. Which reason could be attributed to the difference in query performance?

A. Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.
B. Shuffle Hash Joins are always more efficient than Sort Merge Joins.
C. The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.
D. The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.

Answer: A

Explanation:
Delta Lake outperforms legacy Hadoop formats because it leverages Parquet-based storage, data skipping, and file pruning. According to Databricks documentation, Delta Lake automatically stores detailed statistics (min/max values and file-level metadata) in the transaction log. During query planning, the engine uses these statistics to skip entire files that do not match query filters, a process called data skipping and file pruning. Additionally, Delta uses a vectorized Parquet reader, which reduces I/O and CPU overhead. Together, these optimizations allow Delta to scan significantly less data and produce more efficient physical query plans (e.g., Shuffle Hash Join instead of Sort Merge Join). The performance gain is due to efficient data skipping, not the inherent superiority of join type.

NEW QUESTION # 221
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
B. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
C. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
D. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
E. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.

Answer: E

Explanation:
CDF captures changes only from a Delta table and is only forward-looking once enabled. The CDC logs are writing to object storage. So you would need to ingestion those and merge into downstream tables.

NEW QUESTION # 222
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