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

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

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Databricks Certified Data Engineer Professional Sample Questions (Q164-Q169):

NEW QUESTION # 164
A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?

A. Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
B. Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
C. Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
D. Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
E. Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.

Answer: C

Explanation:
https://www.databricks.com/blog/2017/05/22/running-streaming-jobs-day-10x-cost-savings.html

NEW QUESTION # 165
A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses. Which design approach for the date dimension will support efficient time-based querying and aggregation?

A. Create separate dimension tables for different calendar systems (fiscal, academic, etc.)
B. Store only the date value and calculate all time attributes in queries.
C. Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.
D. Store the date as string in ISO format (YYYY-MM-DD) for readability.

Answer: C

Explanation:
Pre-calculating commonly used time attributes in the date dimension enables efficient filtering, grouping, and aggregation without requiring repeated computations at query time. This design supports a wide range of time-based analyses and improves query performance and usability in dimensional models.

NEW QUESTION # 166
A data engineer is implementing liquid clustering on a Delta Lale table and needs to understand how it affects data management operations. The table will be updated frequently with new data.
The table is an external table and not managed by Unity Catalog. How does liquid clustering in Delta Lake handle new data that is inserted after the initial table creation?

A. New data remains unclustered until the next OPTIMIZE operation.
B. New data is written to a staging area and clustered during scheduled maintenance.
C. New data is rejected if it doesn't match the clustering pattern.
D. New data is automatically clustered during write operations.

Answer: A

Explanation:
With liquid clustering, newly inserted data is written without being immediately reclustered. The clustering layout is applied incrementally during subsequent OPTIMIZE operations, which reorganize both existing and newly added data to maintain an efficient data layout as the table evolves.

NEW QUESTION # 167
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of information is critical to this decision?

A. Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.
B. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
C. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
D. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
E. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.

Answer: C

Explanation:
Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table. While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for long-term versioning needs, making a Type 2 approach more viable for performance and scalability.

NEW QUESTION # 168
A company processes semi-structured JSON files from an external source using Auto Loader in a classic Databricks job. Occasionally, records arrive with null critical fields, invalid types, or unexpected nested schema variations. The engineer must ensure that malformed or non- conforming records are not dropped silently and are captured in a separate quarantine table. The pipeline should continue processing good records into the Bronze layer without failing the job, and the approach must support both batch and streaming ingestion.
The data engineer needs to build a robust ingestion pattern that automatically routes bad records to a quarantine Delta table, while still ingesting good records into the Bronze layer for further processing.
Which approach fulfills the quarantine mechanism in this ingestion architecture?

A. Use Lakeflow Spark Declarative Pipelines with a SQL pipeline; configure it to drop rows with nulls using where critical_fields is not null, and rely on audit logs for malformed data.
B. Use Auto Loader with LDP and implement an EXPECT () constraint with a record audit logic to route bad records.
C. Create a notebook job with inferSchema=True, write a streaming query with .foreachBatch() and catch exceptions using try/except to redirect failed batches to quarantine.
D. Use Auto Loader with failFast mode to set to false, and enable schema evolution; invalid records will be silently ignored during ingestion.

Answer: B

Explanation:
Lakeflow Spark Declarative Pipelines provide built-in support for data quality enforcement using EXPECT clauses. By defining expectations on critical fields and configuring the pipeline to audit or quarantine failed records, malformed or non-conforming JSON records are automatically routed to a quarantine Delta table while valid records continue flowing into the Bronze layer. This approach is robust, auditable, works for both batch and streaming ingestion, and avoids silently dropping bad data or failing the pipeline.

NEW QUESTION # 169
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