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

Section	Objectives
Topic 1: Developing Code for Data Processing using Python and SQL	- Building and Testing ETL Pipelines 1. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 2. Compare streaming tables and materialized views 3. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 4. Configure environments, dependencies, memory, and retry behavior 5. Develop unit and integration tests for data processing code 6. Use APPLY CHANGES APIs for change data capture 7. Use control flow operators in pipeline components 8. Create and automate ETL workloads using Jobs through UI, APIs, and CLI - Using Python and Tools for Development 1. Develop User-Defined Functions using Pandas/Python UDFs 2. Manage and troubleshoot third-party library installations and dependencies 3. Design and implement scalable Python project structures optimized for Databricks Asset Bundles
Topic 2: Monitoring and Alerting	- Alerting 1. Use SQL Alerts for data quality monitoring 2. Configure Lakeflow Jobs notifications for job status and performance issues - Monitoring 1. Use system tables for resource, cost, audit, and workload monitoring 2. Use Query Profiler and Spark UI to monitor workloads 3. Use Databricks REST APIs and CLI for monitoring jobs and pipelines 4. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
Topic 3: Debugging and Deploying	- Debugging and Troubleshooting 1. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics 2. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging 3. Analyze errors and remediate failed job runs - Deploying CI/CD 1. Build and deploy Databricks resources using Databricks Asset Bundles 2. Integrate Git-based CI/CD workflows using Databricks Git Folders
Topic 4: Data Transformation, Cleansing, and Quality	- Advanced Data Transformation 1. Apply window functions, joins, and aggregations to large datasets 2. Write efficient Spark SQL and PySpark transformations - Data Quality 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader 2. Develop data quarantining processes for invalid data
Topic 5: Data Governance	- Unity Catalog Permissions 1. Understand the Unity Catalog permission inheritance model - Metadata and Discoverability 1. Create and maintain descriptions and metadata for enterprise data
Topic 6: Cost & Performance Optimisation	- Query Performance 1. Identify inefficient joins and excessive data shuffling 2. Use Query Profile to identify performance bottlenecks - Delta Optimization 1. Apply data skipping and file pruning techniques 2. Use Change Data Feed to address streaming table limitations and improve latency 3. Understand deletion vectors and liquid clustering - Cost Optimization 1. Understand how Unity Catalog managed tables reduce operational overhead
Topic 7: Data Sharing and Federation	- Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance - Delta Sharing 1. Share live Lakehouse data with external computing platforms 2. Configure Databricks-to-Databricks Sharing 3. Configure sharing with external platforms using the open sharing protocol
Topic 8: Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data 2. Build append-only pipelines for batch and streaming data using Delta 3. Ingest data from message buses and cloud storage
Topic 9: Ensuring Data Security and Compliance	- Compliance 1. Implement pipelines that detect and mask personally identifiable information 2. Develop data purging solutions according to data retention policies - Data Security 1. Use row filters and column masks for sensitive data 2. Use ACLs to secure workspace objects and enforce least privilege 3. Apply anonymization and pseudonymization techniques
Topic 10: Data Modelling	- Dimensional Modelling 1. Design dimensional models for analytical workloads - Scalable Data Models 1. Understand Liquid Clustering versus partitioning and Z-Ordering 2. Optimize data layout using Liquid Clustering 3. Design and implement scalable data models using Delta Lake

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Databricks Certified Data Engineer Professional Sample Questions (Q62-Q67):

NEW QUESTION # 62
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

A. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
B. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
C. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
D. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
E. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.

Answer: A

Explanation:
When a Databricks job runs multiple tasks with dependencies, the tasks are executed in a dependency graph. If a task fails, the downstream tasks that depend on it are skipped and marked as Upstream failed. However, the failed task may have already committed some changes to the Lakehouse before the failure occurred, and those changes are not rolled back automatically. Therefore, the job run may result in a partial update of the Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake to ensure that the changes are only committed when the entire job run succeeds. Alternatively, you can use the Run if condition to configure tasks to run even when some or all of their dependencies have failed, allowing your job to recover from failures and continue running.

NEW QUESTION # 63
Which statement describes integration testing?

A. Validates behavior of individual elements of your application
B. Validates interactions between subsystems of your application
C. Requires an automated testing framework
D. Requires manual intervention
E. Validates an application use case

Answer: B

Explanation:
Integration testing is a type of software testing where components of the software are gradually integrated and then tested as a unified group.

NEW QUESTION # 64
A data engineer, while designing a Pandas UDF to process financial time-series data with complex calculations that require maintaining state across rows within each stock symbol group, must ensure the function is efficient and scalable. Which approach will solve the problem with minimum overhead while preserving data integrity?

A. Use a SCALAR_ITER Pandas UDF with iterator-based processing, implementing state management through persistent storage (Delta tables) that gets updated after each batch to maintain continuity across iterator chunks.
B. Use a grouped_agg Pandas UDF that processes each stock symbol group independently, maintaining state through intermediate aggregation results that get passed between successive UDF calls via broadcast variables.
C. Use a SCALAR Pandas UDF that processes the entire dataset at once, implementing custom partitioning logic within the UDF to group by stock symbol and maintain state using global variables shared across all executor processes.
D. Use applyInPandas() on a Spark DataFrame that receives all rows for each stock symbol as a Pandas DataFrame, allowing processing within each group while maintaining state variables local to each group's processing function.

Answer: D

Explanation:
The Databricks documentation recommends applyInPandas() for complex per-group operations where maintaining internal state within each group is necessary. When using applyInPandas(), Spark provides all records for each grouping key as a Pandas DataFrame to the function, allowing efficient vectorized operations with local state management. This approach ensures high performance and scalability while maintaining logical isolation between groups. In contrast, SCALAR and SCALAR_ITER UDFs operate on individual rows or batches and cannot maintain inter-row state effectively. grouped_agg UDFs are limited to computing aggregates and do not support complex multi-row transformations. Therefore, applyInPandas() is the correct and Databricks-recommended solution for stateful per-group time-series computations.

NEW QUESTION # 65
A platform team lead is responsible for automating the individual teams attribution towards SQL Warehouse usage. The requirement is to identify the SQL warehouse usage at the individual user's level and generate a daily report to be shared with an executive team that includes leaders from all business units. How should the platform lead generate an automated report that can be shared daily?

A. Use the system tables to capture the audit and billing usage data and share the queries with the executive team. This enables the executives to execute the query and see the latest results any time.
B. Let the users run the SQL query and then directly report the usage to the executives. The ownership of the SQL warehouse usage will be with the individual teams.
C. Restrict users from running any SQL query unless they provide all the query details so that the attribution can be calculated and shared with the executive team.
D. Use the system tables to capture the audit and billing usage data and create a dashboard with daily refresh schedules and shared with the executive team.

Answer: D

Explanation:
System tables provide authoritative audit and billing data needed for per-user SQL Warehouse attribution. Creating a dashboard with a scheduled daily refresh automates report generation and ensures executives receive consistent, up-to-date insights without needing to run queries themselves.

NEW QUESTION # 66
A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a "dev" target to be the default and override specific paths. How should the team use databricks.yml to satisfy these requirements?

A. Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.
B. Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default:true and override workspace paths or artifacts under that target.
C. Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.
D. Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.

Answer: B

Explanation:
In Databricks Asset Bundles, the databricks.yml file defines all top-level configuration keys, including bundle, artifacts, workspace, run_as, and targets. The targets section defines specific deployment contexts (for example, dev, test, prod). Setting default: true for a target marks it as the default environment. Overrides for workspace paths and artifact configurations can be defined inside each target while keeping defaults at the top level.

NEW QUESTION # 67
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