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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure and govern Unity Catalog objects | 15β20% | - Implement data governance and security
|
| Topic 2: Set up and configure an Azure Databricks environment | 15β20% | - Integrate with Azure services
|
| Topic 3: Deploy and maintain data pipelines and workloads | 30β35% | - Build and orchestrate pipelines
|
| Topic 4: Prepare and process data | 30β35% | - Ingest and transform data
|
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NEW QUESTION # 43
You have an Azure Databricks workspace named Workspace1.
You create a compute cluster named Cluster1 that will be used to ingest data.
You need to install the required libraries on Cluster1. The solution must use Unity Catalog for access control.
What should you do?
Answer: A
Explanation:
The best action is uploading the libraries to the workspace and installing the libraries on the cluster (or ideally uploading them to Unity Catalog volumes).
Unity Catalog Compatibility: When using Unity Catalog for access control, compute clusters are typically configured with Standard (Shared) access mode. In this mode, traditional cluster init scripts [Not B.] face strict execution restrictions or are completely blocked to maintain secure user isolation.
Governance: Uploading your packages as Workspace Files or to Unity Catalog volumes allows administrators to manage access permissions directly and add them to an allowlist if needed.
Cluster-Wide Availability: Installing the libraries via the cluster's Libraries tab ensures that the required ingestion packages are automatically pre-installed and available across all nodes and notebooks running on that cluster.
Incorrect:
[Not A]
Running pip3 install manually on a cluster terminal or inside a notebook only applies to the specific notebook session (notebook-scoped). It does not natively persist across cluster restarts or handle cross-node execution effectively for data ingestion pipelines.
[Not B]
Running a custom script or a legacy init script to modify system-level paths introduces security risks and is generally incompatible with Unity Catalog's strict execution isolation policies for shared compute.
Reference:
https://docs.databricks.com/aws/en/libraries/
NEW QUESTION # 44
You use Databricks Asset Bundles to manage two jobs and an app.
You need to deploy the bundle to development and production environments. The solution must meet the following requirements
* Deploy the app to both environments.
* Deploy only one job to development.
* Minimize administrative effort.
What should you use?
Answer: B
Explanation:
The correct answer is D - a targets node in databricks.yml.
Databricks Asset Bundles use a single databricks.yml to define all resources (jobs, apps, pipelines) once, and a targets node to define per-environment overrides. Within the development target, you can use the include
/exclude mechanism or resource-level overrides to deploy only one of the two jobs. The app and the second job are deployed to both environments through the shared resource definition.
Option B (separate databricks.yml files per environment) works technically but means duplicating the shared resource definitions across files - any change to a shared resource requires edits in multiple places, which is exactly the administrative overhead the question wants to avoid.
Option A (resources node) defines resources globally across all targets - it doesn't provide environment- specific filtering. Option C (variables node) parameterises values like cluster sizes or paths but doesn't control which resources are deployed to which environment.
Reference: https://learn.microsoft.com/en-us/azure/databricks/dev-tools/bundles/deployment-modes
NEW QUESTION # 45
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 processes raw data files stored in Azure Storage.
New files arrive at unpredictable intervals.
You need to ensure that Job1 starts automatically when new files arrive and does NOT consume compute resources when no data is available.
Which type of job trigger should you use?
Answer: C
Explanation:
The File arrival trigger is exactly what you need for this use case. It natively solves your problem by launching a job the moment new files land in your Azure Storage container, automatically scaling down to zero compute when no data is processing.
Event-Driven: Instead of relying on a time-based schedule (cron) that wastes resources, the Lakeflow Job monitors the specific Azure location and starts exactly when new data appears.
Zero Idle Compute: You can use serverless compute or an ephemeral job cluster that spins up just for the duration of the run and terminates immediately after, meaning zero billing during downtime.
Cloud Cost Efficient: Listing files to detect new arrivals is typically free or fractions of a cent (only incurring minimal cloud provider API costs for listing the storage location).
Reference:
https://learn.microsoft.com/en-us/azure/databricks/jobs/file-arrival-triggers
NEW QUESTION # 46
Drag and Drop Question
You have an Azure Databricks workspace that contains an all-purpose compute cluster named Cluster1. Cluser1 is used for interactive development.
You need to configure Cluster1 to meet the following requirements:
- Automatically add and remove worker nodes based on workload demand.
- Automatically shut down when the cluster has been idle for a specific period.
What should you configure for each requirement? To answer, drag the appropriate options to the correct requirements. Each option may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 47
Hotspot Question
You have an Azure Databricks job named Job1 that contains an ingestion task named Task1 and transformation task named Task2.
You need to ensure that if Task1 fails, the task retries automatically, and Task2 is prevented from running.
How should you configure Job1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 48
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