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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modelling | 6% | - Schema design and management - Delta Lake table design - Medallion Architecture implementation |
| Topic 2: Developing Code for Data Processing using Python and SQL | 22% | - Data transformation and aggregation - Integration with Databricks APIs and tools - Batch and incremental processing logic |
| Topic 3: Data Governance | 7% | - Unity Catalog management - Data lineage and metadata tracking - Policy enforcement |
| Topic 4: Data Ingestion & Acquisition | 7% | - Auto Loader and streaming ingestion - Connecting to diverse data sources - Schema inference and evolution |
| Topic 5: Debugging and Deploying | 10% | - Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices |
| Topic 6: Ensuring Data Security and Compliance | 10% | - Compliance standards implementation - Access control and permissions - Data encryption and masking |
| Topic 7: Cost & Performance Optimisation | 13% | - Cluster configuration and scaling - Query optimization and caching - Storage optimization (partitioning, Z-order, indexing) |
| Topic 8: Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
| Topic 9: Monitoring and Alerting | 10% | - Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications |
| Topic 10: Data Transformation, Cleansing, and Quality | 10% | - Standardization and normalization - Handling missing or inconsistent data - Data validation and quality checks |
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NEW QUESTION # 202
Which of the following Structured Streaming queries is performing a hop from a Bronze table to a Silver
table?
Answer: C
NEW QUESTION # 203
You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion so your job can run immediatley?
Answer: B
Explanation:
Explanation
Cluster pools allow us to reserve VM's ahead of time, when a new job cluster is created VM are grabbed from the pool. Note: when the VM's are waiting to be used by the cluster only cost incurred is Azure. Databricks run time cost is only billed once VM is allocated to a cluster.
Here is a demo of how to setup a pool and follow some best practices,
Graphical user interface, text Description automatically generated
NEW QUESTION # 204
The Databricks CLI is used to trigger a run of an existing job by passing the job_id parameter. The response indicating the job run request was submitted successfully includes a field run_id. Which statement describes what the number alongside this field represents?
Answer: D
Explanation:
* Exact extract: "run_id: The canonical identifier of a run."
References: Databricks Jobs API/CLI response fields.
NEW QUESTION # 205
A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline.
How should the data engineer perform this syntax check?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks provides a "Validate" option within the Lakeflow Declarative Pipeline development interface that checks pipeline configurations, transformations, and syntax errors before actual execution.
This feature parses and validates the pipeline logic defined in notebooks or workspace files to ensure correctness and consistency of table dependencies, DLT (Delta Live Table) syntax, and schema references.
The validation process does not process or move any data, making it ideal for testing new configurations before deployment.
Using the shell terminal (B) or workspace files (D) does not perform integrated pipeline-level validation, while reconnecting to compute clusters (C) is unrelated to syntax checks. Therefore, the verified and correct approach is A.
NEW QUESTION # 206
The Delta Live Tables Pipeline is configured to run in Development mode using the Triggered Pipeline Mode.
what is the expected outcome after clicking Start to update the pipeline?
Answer: E
Explanation:
Explanation
The answer is All datasets will be updated once and the pipeline will shut down. The compute re-sources will persist to allow for additional testing.
DLT pipeline supports two modes Development and Production, you can switch between the two based on the stage of your development and deployment lifecycle.
Development and production modes
When you run your pipeline in development mode, the Delta Live Tables system:
*Reuses a cluster to avoid the overhead of restarts.
*Disables pipeline retries so you can immediately detect and fix errors.
In production mode, the Delta Live Tables system:
*Restarts the cluster for specific recoverable errors, including memory leaks and stale credentials.
*Retries execution in the event of specific errors, for example, a failure to start a cluster.
Use the buttons in the Pipelines UI to switch between develop-ment and production modes. By default, pipelines run in development mode.
Switching between development and production modes only controls cluster and pipeline execution behavior.
Storage locations must be configured as part of pipeline settings and are not affected when switching between modes.
Please review additional DLT concepts using below link
https://docs.databricks.com/data-engineering/delta-live-tables/delta-live-tables-concepts.html#delta-live-tables-c
NEW QUESTION # 207
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