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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security and Governance | ~10% | - Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs |
| Topic 2: Data Modeling | ~10% | - Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques |
| Topic 3: Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Topic 4: Cost and Performance Optimization | ~13% | - Optimize queries, clusters, and storage - Leverage system tables and observability tools |
| Topic 5: Streaming Workloads and Change Data Capture | ~11% | - Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines |
| Topic 6: Data Transformation, Cleansing, and Quality | ~12% | - Apply advanced Spark transformations - Enforce data quality and quarantine bad data |
| Topic 7: CI/CD, Testing, and Deployment | ~6% | - Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines |
| Topic 8: Developing Code for Data Processing using Python and SQL | ~22% | - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Implement scalable Python/SQL code and project structures - Manage dependencies, libraries, and UDFs |
| Topic 9: Monitoring, Logging, and Troubleshooting | ~8% | - Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables |
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NEW QUESTION # 106
A data engineer is implementing a job to download multiple PDF files from a third-party provided REST API endpoint by specifying different report types. The REST API is time-consuming and encounters intermittent errors, so the engineer wants to track each download activity to know when it fails and to retry partially, while providing scalable throughput. The engineer needs to download ten report types, and the list can be changed over time. How should the data engineer achieve this?
Answer: B
Explanation:
A foreach task allows the job to dynamically iterate over a configurable list of report types, execute downloads in parallel, and track the success or failure of each item independently. This enables scalable throughput, partial retries for failed downloads, and easy updates when the list of report types changes, without hardcoding tasks or introducing unnecessary complexity.
NEW QUESTION # 107
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?
Answer: D
Explanation:
Installing a Python package scoped at the notebook level to all nodes in the currently active cluster in Databricks can be achieved by using the Libraries tab in the cluster UI. This interface allows you to install libraries across all nodes in the cluster. While the %pip command in a notebook cell would only affect the driver node, using the cluster UI ensures that the package is installed on all nodes.
NEW QUESTION # 108
A data engineer needs to implement column masking for a sensitive column in a Unity Catalog- managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments. Which approach should the engineer use to efficiently enforce this requirement?
Answer: A
Explanation:
Databricks Unity Catalog supports dynamic column masking, where masking logic can be implemented using SQL functions or UDFs that reference external mapping tables or metadata for context-aware access control.
By referencing the group_access table inside the masking function, the mask dynamically evaluates whether a requesting user belongs to an authorized group. If permitted, the original column value is returned; otherwise, a masked value (such as NULL or asterisks) is shown.
This method enables fine-grained, data-driven masking policies while maintaining a single authoritative access mapping source.
Hardcoding values (A) reduces flexibility, and row filters (D) apply to entire rows rather than specific columns. Therefore, C correctly aligns with Databricks best practices for dynamic masking.
NEW QUESTION # 109
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?
Answer: B
Explanation:
Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.
NEW QUESTION # 110
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: A
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 # 111
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