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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cost & Performance Optimisation | 13% | - Improve query and pipeline performance - Optimize compute and storage resources - Apply cost management best practices |
| Topic 2: Data Sharing and Federation | 5% | - Manage cross-platform data access - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation |
| Topic 3: Data Modelling | 6% | - Design Medallion Architecture - Implement dimensional and relational models - Optimize table design and partitioning |
| Topic 4: Data Governance | 7% | - Enforce data policies and standards - Manage data assets and metadata - Use Unity Catalog for governance |
| Topic 5: Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines |
| Topic 6: Ensuring Data Security and Compliance | 10% | - Ensure data privacy and compliance - Secure data at rest and in transit - Implement access control and permissions |
| Topic 7: Data Transformation, Cleansing, and Quality | 10% | - Implement schema evolution and management - Enforce data quality standards - Apply data cleansing and validation rules |
| Topic 8: Data Ingestion & Acquisition | 7% | - Use Auto Loader and structured streaming - Handle incremental and batch data loads - Ingest data from diverse sources |
| Topic 9: Developing Code for Data Processing using Python and SQL | 22% | - Write efficient and maintainable code - Implement complex data processing logic - Use Databricks-specific libraries and APIs |
| Topic 10: Monitoring and Alerting | 10% | - Set up alerts and notifications - Monitor pipeline performance and health - Track data lineage and metrics |
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NEW QUESTION # 243
A data engineer is evaluating tools to build a production-grade data pipeline. The team must process change data from cloud object storage, filter out or isolate invalid records, and ensure the timely delivery of clean data to downstream consumers. The team is small, under tight deadlines, and wants to minimize operational overhead while keeping pipelines auditable and maintainable.
Which approach should the data engineer implement?
Answer: C
Explanation:
LDP provides a declarative framework for building production-grade pipelines with minimal operational overhead. Streaming Tables and Materialized Views handle incremental processing automatically, while built-in data expectations allow invalid records to be filtered or isolated in a consistent and auditable way. This approach is well suited for small teams under tight deadlines, as it simplifies maintenance, improves reliability, and ensures timely delivery of clean data to downstream consumers.
NEW QUESTION # 244
A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context.
Assuming the job is already deployed, they need to trigger its execution while ensuring the target- specific configuration is respected. Which command will trigger the job execution?
Answer: B
Explanation:
Databricks Asset Bundles (DABs) enable declarative configuration and deployment of Databricks resources such as jobs, pipelines, and dashboards across multiple environments.
Once deployed, jobs can be executed in a specific target context using the databricks bundle run command, which ensures all environment-specific configurations from the bundle definition (such as parameters, cluster settings, and workspace URLs) are respected.
The -t flag specifies the target environment (e.g., dev, staging, or prod). This ensures that the execution runs with the correct configuration defined under that target in databricks.yml.
Other options (A, B, and C) are invalid because they reference deprecated or incorrect command syntax that doesn't integrate with bundle targets. Therefore, D is the correct and verified answer.
NEW QUESTION # 245
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?
Answer: E
NEW QUESTION # 246
Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?
Answer: B
Explanation:
DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems. DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources. By Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container. Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting. Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled. The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace. Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted.
NEW QUESTION # 247
A data engineering team is collaborating on a Databricks project where each team member needs to develop and test code independently before merging changes into the main branch.
They want to avoid accidental overwrites or branch switching issues while ensuring that all work is version- controlled and can be integrated into their CI/CD pipeline.
How should the data engineer achieve collaboration?
Answer: C
Explanation:
Using separate Databricks Git folders per user mapped to the same remote repository allows each team member to work independently on their own branch without interfering with others.
This prevents accidental overwrites or branch conflicts while ensuring all changes are version- controlled and easily integrated into CI/CD workflows.
NEW QUESTION # 248
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