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

Section	Objectives
Data Modeling	- Design and optimize data models 1. Simplify data layout decisions and optimize query performance using liquid clustering 2. Identify the benefits of liquid clustering over partitioning and Z-Ordering 3. Design and implement scalable data models using Delta Lake to manage large datasets 4. Design dimensional models for analytical workloads with efficient querying and aggregation
Monitoring and Alerting	- Alerting 1. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues 2. Use SQL Alerts to monitor data quality - Monitoring 1. Use Query Profile and Spark UI to monitor workloads 2. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines 3. Use system tables for observability of resource utilization, cost, auditing, and workloads 4. Use Lakeflow Declarative Pipelines event logs to monitor pipelines
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
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
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. Analyze errors and remediate failed job runs using job repairs and parameter overrides 2. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines 3. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors
Data Sharing and Federation	- Share and federate data 1. Configure Lakehouse Federation with appropriate governance across supported source systems 2. Use Delta Sharing to share live data from the Lakehouse with any computing platform 3. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol
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
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. 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
Cost & Performance Optimization	- Optimize cost and performance 1. Understand Delta optimization techniques such as deletion vectors and liquid clustering 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 how and why Unity Catalog managed tables reduce operational overhead and maintenance burden
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. 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. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 4. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 5. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 6. Create pipeline components using control flow operators such as if/else and foreach 7. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 8. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI

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Databricks Certified Data Engineer Professional Sample Questions (Q151-Q156):

NEW QUESTION # 151
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. Use broadcast join.
C. Repartition by region before aggregation.
D. Caching the DataFrame df.

Answer: C

Explanation:
Repartitioning the DataFrame by the grouping key ensures that records with the same region are colocated in the same partitions before the aggregation runs. This significantly reduces the amount of data shuffled during the groupBy operation, leading to more efficient execution.

NEW QUESTION # 152
A data team is automating a daily multi-task ETL pipeline in Databricks. The pipeline includes a notebook for ingesting raw data, a Python wheel task for data transformation, and a SQL query to update aggregates. They want to trigger the pipeline programmatically and see previous runs in the GUI. They need to ensure tasks are retried on failure and stakeholders are notified by email if any task fails. Which two approaches will meet these requirements? (Choose two.)

A. Create a multi-task job using the UI, Databricks Asset Bundles (DABs), or the Jobs REST API (/jobs/create) with notebook, Python wheel, and SQL tasks. Configure task-level retries and email notifications in the job definition.
B. Use Databricks Asset Bundles (DABs) to deploy the workflow, then trigger individual tasks directly by referencing each task's notebook or script path in the workspace.
C. Trigger the job programmatically using the Databricks Jobs REST API (/jobs/run-now), the CLI (databricks jobs run-now), or one of the Databricks SDKs.
D. Use the REST API endpoint /jobs/runs/submit to trigger each task individually as separate job runs and implement retries using custom logic in the orchestrator.
E. Create a single orchestrator notebook that calls each step with dbutils.notebook.run(), defining a job for that notebook and configuring retries and notifications at the notebook level.

Answer: A,C

Explanation:
Databricks Jobs supports defining multi-task workflows that include notebooks, SQL statements, and Python wheel tasks. These can be configured with retry policies, dependency chains, and failure notifications. The correct practice, as stated in the documentation, is to use the Jobs REST API (/jobs/create) or Databricks Asset Bundles to define multi-task jobs, and then trigger them programmatically using /jobs/run-now, CLI, or SDK. This allows the team to maintain full job history, handle retries automatically, and receive alerts via configured email notifications. Using
/jobs/runs/submit creates one-off ad hoc runs without maintaining dependency visibility.
Therefore, options B and C together satisfy the operational, automation, and governance requirements.

NEW QUESTION # 153
A senior data engineer is planning large-scale data workflows. The current task is to identify the considerations that form a foundation for creating scalable data models that are essential for effective management of large datasets. The data engineering team has identified the core capabilities as part of a scalable data model to build a modern data platform and provided their reasoning for considering Delta Lake for review. The senior data engineer is responsible for identifying the recommendations that are not valid. Which key features can be ignored while evaluating Delta Lake?

A. Delta Lake's capability to process data in both batch and streaming modes seamlessly, providing flexibility in data ingestion and processing.
B. Delta Lake provides limited support for monitoring and troubleshooting data pipelines, so relevant partner tools have to be identified and set up for enhanced operational efficiency.
C. Delta Lake works with various data formats (e.g., Parquet, JSON, CSV) and integrates well with Spark and Databricks tools.
D. Delta Lake optimizes metadata handling, efficiently managing billions of files and facilitating scalability to petabyte-scale datasets.

Answer: B

Explanation:
Delta Lake includes built-in capabilities for monitoring, auditing, and troubleshooting through transaction logs, history, and tight integration with the Databricks platform. Therefore, limited support for monitoring and troubleshooting is not a valid concern when evaluating Delta Lake and can be ignored.

NEW QUESTION # 154
A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

A. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
B. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
C. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
D. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
E. Merge all changes back to the main branch in the remote Git repository and clone the repo again

Answer: D

Explanation:
This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook.

NEW QUESTION # 155
A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
B. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
C. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
D. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
E. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.

Answer: C

NEW QUESTION # 156
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