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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Implement schema evolution and management - Enforce data quality standards |
| Topic 2: Data Modelling | 6% | - Optimize table design and partitioning - Design Medallion Architecture - Implement dimensional and relational models |
| Topic 3: Data Ingestion & Acquisition | 7% | - Handle incremental and batch data loads - Use Auto Loader and structured streaming - Ingest data from diverse sources |
| Topic 4: Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines |
| Topic 5: Data Governance | 7% | - Use Unity Catalog for governance - Manage data assets and metadata - Enforce data policies and standards |
| Topic 6: Developing Code for Data Processing using Python and SQL | 22% | - Use Databricks-specific libraries and APIs - Write efficient and maintainable code - Implement complex data processing logic |
| Topic 7: Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation - Manage cross-platform data access |
| Topic 8: Cost & Performance Optimisation | 13% | - Improve query and pipeline performance - Apply cost management best practices - Optimize compute and storage resources |
| Topic 9: Monitoring and Alerting | 10% | - Set up alerts and notifications - Monitor pipeline performance and health - Track data lineage and metrics |
| Topic 10: Ensuring Data Security and Compliance | 10% | - Implement access control and permissions - Ensure data privacy and compliance - Secure data at rest and in transit |
Databricks Certified Data Engineer Professional Sample Questions:
1. The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer's suggestion is correct?
A) ZORDER ON review will need to be run to see performance gains.
B) Delta Lake statistics are not optimized for free text fields with high cardinality.
C) The Delta log creates a term matrix for free text fields to support selective filtering.
D) Text data cannot be stored with Delta Lake.
E) Delta Lake statistics are only collected on the first 4 columns in a table.
2. A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:
Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?
A) Yes; both of the streams can share a single checkpoint directory.
B) No; each of the streams needs to have its own checkpoint directory.
C) No; Delta Lake manages streaming checkpoints in the transaction log.
D) Yes; Delta Lake supports infinite concurrent writers.
E) No; only one stream can write to a Delta Lake table.
3. A data engineer is working on a Databricks notebook that requires several third-party Python libraries. Some of these are available on PyPI, while others are custom-developed and stored as local.wheel (.whl) and source (.tar.gz) files in an S3 bucket. The goal is to ensure all dependencies are installed and correctly available across multiple jobs running on any automated cluster in a Unity Catalog-enabled workspace. The engineer needs to install the required dependencies in a way that ensures a consistent environment setup across interactive notebooks and jobs and complies with workspace security policies (no internet access). Which approach should the engineer use to install and manage these dependencies while also ensuring reproducibility and compliance?
A) Use an init script on the cluster to install all dependencies using pip, referencing the local file system.
B) Create a Python wheel file for the entire project, upload it to the Databricks Workspace Files or Volumes, and install it using a Cluster Library or pip install in a requirements.txt declared within a Databricks Asset Bundle.
C) Use %pip install in every notebook and job to install packages directly from PyPl and custom S3 paths.
D) Install all dependencies manually in the driver node of an interactive cluster, then export the environment and reimport on job clusters using %conda.
4. A data engineer has created a new cluster using shared access mode with default configurations.
The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?
A) CAN VIEW
B) CAN RESTART
C) CAN ATTACH TO
D) CAN MANAGE
5. A data engineer is attempting to execute the following PySpark code:
df = spark.read.table("sales")
result = df.groupBy("region").agg(sum("revenue"))
However, upon inspecting the execution plan and profiling the Spark job, they observe excessive data shuffling during the aggregation phase.
Which technique should be applied to reduce shuffling during the groupBy aggregation operation?
A) Use coalesce() after the aggregation.
B) Caching the DataFrame df.
C) Use broadcast join.
D) Repartition by region before aggregation.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: D |
