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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformation, Cleansing, and Quality | 10% | - Enforce data quality standards - Apply data cleansing and validation rules - Implement schema evolution and management |
| Topic 2: Cost & Performance Optimisation | 13% | - Improve query and pipeline performance - Optimize compute and storage resources - Apply cost management best practices |
| Topic 3: Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation - Manage cross-platform data access |
| Topic 4: Data Ingestion & Acquisition | 7% | - Use Auto Loader and structured streaming - Ingest data from diverse sources - Handle incremental and batch data loads |
| Topic 5: Data Modelling | 6% | - Optimize table design and partitioning - Implement dimensional and relational models - Design Medallion Architecture |
| Topic 6: Data Governance | 7% | - Manage data assets and metadata - Use Unity Catalog for governance - Enforce data policies and standards |
| Topic 7: Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs |
| Topic 8: Ensuring Data Security and Compliance | 10% | - Secure data at rest and in transit - Implement access control and permissions - Ensure data privacy and compliance |
| Topic 9: Monitoring and Alerting | 10% | - Monitor pipeline performance and health - Track data lineage and metrics - Set up alerts and notifications |
| Topic 10: 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 |
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NEW QUESTION # 209
A data engineering team is setting up deployment automation. To deploy workspace assets remotely using the Databricks CLI command, they must configure it with proper authentication.
Which authentication approach will provide the highest level of security?
Answer: B
Explanation:
The most secure and enterprise-recommended authentication method for Databricks automation is OAuth token federation with service principals.
This configuration allows service principals (non-human identities) to authenticate using temporary OAuth access tokens from a trusted identity provider (such as Azure AD or AWS IAM federation). These tokens are short-lived and scoped, significantly reducing credential exposure risks.
By contrast, static client secrets (B) or PATs (C) are long-lived and require periodic manual rotation, increasing security vulnerability. Shared user accounts (D) violate least-privilege and auditability principles. Therefore, A provides the strongest, most compliant authentication model for automated CLI and CI/CD workflows.
NEW QUESTION # 210
Which statement regarding spark configuration on the Databricks platform is true?
Answer: B
Explanation:
When Spark configuration properties are set for an interactive cluster using the Clusters UI in Databricks, those configurations are applied at the cluster level. This means that all notebooks attached to that cluster will inherit and be affected by these configurations. This approach ensures consistency across all executions within that cluster, as the Spark configuration properties dictate aspects such as memory allocation, number of executors, and other vital execution parameters. This centralized configuration management helps maintain standardized execution environments across different notebooks, aiding in debugging and performance optimization.
NEW QUESTION # 211
A data engineer manages a production Lakeflow Declarative Pipeline that processes customer transaction data. The pipeline includes several data quality expectations such as transaction_amount > 0 and customer_id IS NOT NULL. These expectations are defined using the EXPECT clause in SQL.
The engineer aims to monitor the pipeline's data quality by analyzing the number of records that passed or failed each expectation during the latest pipeline update. The Lakeflow Declarative Pipelines event logs are stored in a Delta table named event_log_table.
For the most recent pipeline update, determine a programmatically appropriate approach to extract information like the name of each expectation, associated dataset, count of records that passed the expectation, and count of records that failed the expectation.
Which method retrieves the desired data quality metrics from the Lakeflow Declarative Pipelines event log?
Answer: B
Explanation:
The Databricks documentation specifies that for Lakeflow Declarative Pipelines, detailed data quality metrics are logged as events of type expectation_result within the event log. Each record of this type contains fields including expectation_name, dataset_name, passed_records, and failed_records. Filtering on event_type = 'expectation_result' and expanding the details field allows retrieving metrics for each expectation from the most recent pipeline update. While flow_progress provides summary statistics and data_quality events aggregate results, only expectation_result events provide granular, per-expectation metrics required for audit and monitoring automation.
NEW QUESTION # 212
In order to facilitate near real-time workloads, a data engineer is creating a helper function to Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:
Which response correctly fills in the blank to meet the specified requirements?
Answer: A
Explanation:
https://docs.databricks.com/en/ingestion/auto-loader/schema.html
NEW QUESTION # 213
A data engineering team has a time-consuming data ingestion job with three data sources. Each notebook takes about one hour to load new data. One day, the job fails because a notebook update introduced a new required configuration parameter. The team must quickly fix the issue and load the latest data from the failing source. Which action should the team take?
Answer: A
Explanation:
The repair run capability in Databricks Jobs allows re-execution of failed tasks without re-running successful ones. When a parameterized job fails due to missing or incorrect task configuration, engineers can perform a repair run to fix inputs or parameters and resume from the failed state.
This approach saves time, reduces cost, and ensures workflow continuity by avoiding unnecessary recomputation. Additionally, updating the task definition with the missing parameter prevents future runs from failing.
Running the job manually (B) loses run context; (C) alone does not prevent recurrence; (D) delays resolution. Thus, A follows the correct operational and recovery practice.
NEW QUESTION # 214
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