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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developing Code for Data Processing using Python and SQL | 22% | - Batch and incremental processing logic - Data transformation and aggregation - Integration with Databricks APIs and tools |
| Topic 2: Data Governance | 7% | - Policy enforcement - Data lineage and metadata tracking - Unity Catalog management |
| Topic 3: Debugging and Deploying | 10% | - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors |
| Topic 4: Cost & Performance Optimisation | 13% | - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching |
| Topic 5: Data Ingestion & Acquisition | 7% | - Schema inference and evolution - Auto Loader and streaming ingestion - Connecting to diverse data sources |
| Topic 6: Monitoring and Alerting | 10% | - Pipeline observability and logging - Setting up alerts and notifications - Performance and health monitoring |
| Topic 7: Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Topic 8: Data Transformation, Cleansing, and Quality | 10% | - Standardization and normalization - Data validation and quality checks - Handling missing or inconsistent data |
| Topic 9: Data Modelling | 6% | - Medallion Architecture implementation - Delta Lake table design - Schema design and management |
| Topic 10: Ensuring Data Security and Compliance | 10% | - Compliance standards implementation - Data encryption and masking - Access control and permissions |
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NEW QUESTION # 165
Which of the following is a correct statement on how the data is organized in the storage when when managing a DELTA table?
Answer: E
Explanation:
Explanation
Answer is
All of the data is broken down into one or many parquet files, log files are broken down into one or many json files, and each transaction creates a new data file(s) and log file.
here is sample layout of how DELTA table might look,
NEW QUESTION # 166
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: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
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 # 167
A table in the Lakehouse namedcustomer_churn_paramsis used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?
Answer: D
Explanation:
Explanation
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with an existing cluster id and a notebook task, but also specifies a new cluster spec with some configurations. According to the documentation, if both an existing cluster id and a new cluster spec are provided, then a new cluster will be created for each run of the job with those configurations, and then terminated after completion. Therefore, the logic defined in the referenced notebook will be executed three times on new clusters with those configurations. Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; Databricks Documentation, under
"JobsClusterSpecNewCluster" section.
NEW QUESTION # 168
A data engineer has created a new cluster using shared access mode with default configurations. The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?
Answer: B
Explanation:
Databricks provides different permission levels to control access to clusters. The correct minimal permission required for viewing driver logs is CAN VIEW.
Databricks Cluster Permission Levels:
CAN ATTACH TO:
Allows users to attach notebooks to a cluster but does not allow them to view logs.
Not sufficient for viewing driver logs.
CAN MANAGE:
Grants full control over the cluster, including starting, stopping, and editing configurations.
Too broad for this requirement.
CAN VIEW (Correct Answer):
Allows users to view cluster details, logs, and status but not modify any configurations.
Minimal required permission for viewing logs.
CAN RESTART:
Grants permission to restart the cluster, but does not include log access.
Not sufficient for viewing logs.
Conclusion:
The minimal permission needed to allow the development team to view driver logs is CAN VIEW.
Reference:
Databricks Cluster Permissions Documentation
NEW QUESTION # 169
Which of the following Structured Streaming queries is performing a hop from a bronze table to a Silver table?
Answer: A
Explanation:
Explanation
A diagram of a house Description automatically generated with low confidence
NEW QUESTION # 170
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