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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. 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
Topic 2: Data Modeling	- Design and optimize data models 1. Simplify data layout decisions and optimize query performance using liquid clustering 2. Design and implement scalable data models using Delta Lake to manage large datasets 3. Design dimensional models for analytical workloads with efficient querying and aggregation 4. Identify the benefits of liquid clustering over partitioning and Z-Ordering
Topic 3: 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 4: 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 5: Developing Code for Data Processing using Python and SQL	- Using Python and Tools for Development 1. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration 2. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives 3. Develop User-Defined Functions using Pandas/Python UDF - 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. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 3. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 4. Create pipeline components using control flow operators such as if/else and foreach 5. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI 6. Explain the advantages and disadvantages of streaming tables compared to materialized views 7. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 8. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader
Topic 6: 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 7: 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. Understand Delta optimization techniques such as deletion vectors and liquid clustering 4. Apply Change Data Feed to address streaming table limitations and improve latency 5. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling
Topic 8: Debugging and Deploying	- Deploying CI/CD 1. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment 2. Build and deploy Databricks resources using Databricks Asset Bundles - 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
Topic 9: Monitoring and Alerting	- 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 Databricks REST APIs and Databricks CLI to monitor jobs and pipelines 4. Use Query Profile and Spark UI to monitor workloads - 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
Topic 10: Data Governance	- Govern enterprise data 1. Create and add descriptions and metadata to enterprise data to improve discoverability 2. Demonstrate understanding of the Unity Catalog permission inheritance model

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Databricks Certified Data Engineer Professional 認定 Certified-Data-Engineer-Professional 試験問題 (Q164-Q169):

質問 # 164
A data engineer needs to install the PyYAML Python package within an air-gapped Databricks environment. The workspace has no direct internet access to PyPI. The engineer has downloaded the .whl file locally and wants it available automatically on all new clusters. Which approach should the data engineer use?

A. Upload the PyYAML .whl file to the user home directory and create a cluster-scoped init script to install it.
B. Add the .whl file to Databricks Git Repos and assume automatic installation.
C. Set up a private PyPI repository and install via pip index URL.
D. Upload the PyYAML .whl file to a Unity Catalog Volume, ensure it's allow-listed, and create a cluster-scoped init script that installs it from that path.

正解：D

解説：
For secure, air-gapped Databricks deployments, the recommended practice is to host dependency files such as .whl packages in Unity Catalog Volumes -- a managed storage layer governed by Unity Catalog.
Once stored in a volume, these files can be safely referenced from cluster-scoped init scripts, which automatically execute installation commands (e.g., pip install
/Volumes/catalog/schema/path/PyYAML.whl) during cluster startup.
This ensures consistent environment setup across clusters and compliance with data governance rules.
User directories (A) lack enterprise security controls; private repositories (C) are not viable in air- gapped setups; and Git repos (D) do not trigger package installation. Therefore, B is the correct and officially approved method.

質問 # 165
A data engineer is using Lakeflow Declarative Pipeline to propagate row deletions from a source bronze table (user_bronze) to a target silver table (user_silver). The engineer wants deletions in user_bronze to automatically delete corresponding rows in user_silver during pipeline execution.
Which configuration ensures deletions in the bronze table are propagated to the silver table?

A. Enable CDF on user_silver, read its transaction log, and use MERGE to sync deletions.
B. Use apply_changes without CDF and filter rows where _soft_deleted is true.
C. Enable Change Data Feed (CDF) on user_bronze, read its CDF stream, and use apply_changes() with apply_as_deletes=True for user_silver.
D. Configure VACUUM on user_bronze to delete files, then rebuild user_silver from scratch.

正解：C

解説：
According to Databricks documentation, Change Data Feed (CDF) allows pipelines to read incremental data changes, including inserts, updates, and deletes, from a Delta table. When deletions occur in the source table, reading the CDF stream ensures downstream consumers receive the deletion records. The Lakeflow Declarative Pipelines API provides the apply_changes() function (or auto-CDC pipelines) with the apply_as_deletes parameter to correctly apply those deletions to the target table. This enables automatic synchronization between bronze and silver layers. Options A and D either require manual handling or complete rebuilds, and C incorrectly applies CDF to the target rather than the source. Therefore, enabling CDF on the bronze table and using apply_as_deletes=True is the correct, Databricks-supported configuration.

質問 # 166
Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
B. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
C. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
D. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
E. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.

正解：D

解説：
The key idea in Structured Streaming is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model. You will express your streaming computation as standard batch-like query as on a static table, and Spark runs it as an incremental query on the unbounded input table. Let's understand this model in more detail.
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html

質問 # 167
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A. The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.
B. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
C. Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
D. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
E. Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.

正解：E

解説：
This is the correct answer because it accurately presents information about Delta Lake and Databricks that may impact the decision-making process of a junior data engineer who is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields. Delta Lake and Databricks support schema inference and evolution, which means that they can automatically infer the schema of a table from the source data and allow adding new columns or changing column types without affecting existing queries or pipelines. However, schema inference and evolution may not always be desirable or reliable, especially when dealing with complex or nested data structures or when enforcing data quality and consistency across different systems. Therefore, setting types manually can provide greater assurance of data quality enforcement and avoid potential errors or conflicts due to incompatible or unexpected data types.

質問 # 168
The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Which statement describes what the number alongside this field represents?

A. The job_id is returned in this field.
B. The total number of jobs that have been run in the workspace.
C. The job_id and number of times the job has been are concatenated and returned.
D. The globally unique ID of the newly triggered run.
E. The number of times the job definition has been run in the workspace.

正解：D

解説：
When triggering a job run using the Databricks CLI, the run_id field in the response represents a globally unique identifier for that particular run of the job. This run_id is distinct from the job_id.
While the job_id identifies the job definition and is constant across all runs of that job, the run_id is unique to each execution and is used to track and query the status of that specific job run within the Databricks environment. This distinction allows users to manage and reference individual executions of a job directly.

質問 # 169
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