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| Section | Weight | Objectives |
|---|---|---|
| Secure and govern Unity Catalog objects | 15–20% | - Implement data governance and security
|
| Deploy and maintain data pipelines and workloads | 30–35% | - Build and orchestrate pipelines
|
| Set up and configure an Azure Databricks environment | 15–20% | - Select and configure compute resources
|
| Prepare and process data | 30–35% | - Optimize and manage data storage
|
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NEW QUESTION # 47
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! runs every hour.
Occasionally, the job run takes longer than one hour to complete. Overlapping runs must be prevented to avoid data corruption.
You need to configure the job scheduling behavior.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Two settings address the overlapping-run problem:
Concurrent Runs policy set to 'Skip' (or 'Allow only one concurrent run'). When a new scheduled trigger fires while the previous run is still in progress, the new run is skipped rather than starting alongside the ongoing one. This prevents two runs from writing to the same tables at the same time - which is the data corruption risk the question highlights.
Cron-based schedule for the hourly trigger. A cron expression defines the regular execution cadence.
Combined with the concurrency setting, the job runs hourly but never overlaps.
An alternative to 'Skip' is 'Wait' (queue the new run), which ensures every scheduled run eventually executes
- but for this scenario where overlapping is the primary concern, skipping the missed run is typically preferable to building up a queue of back-to-back executions.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#concurrent-runs
NEW QUESTION # 48
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to create an external volume named Volume1 in an existing schema. Volume1 must expose files from an Azure Storage container. The solution must meet the following requirements:
* Ensure that authentication does NOT require storing credentials in Databricks
* Ensure that users can access the files, but NOT modify the files.
* Follow the principle of least privilege
Which type of authentication should you configure, and which permission should you grant to the users? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For authentication, a Managed Identity (via a Databricks Access Connector) is the right choice. The Access Connector wraps an Azure-managed identity so Databricks can authenticate to Azure Storage without any credentials being stored in the workspace. The cloud security team controls the identity through Azure RBAC
- there are no secrets to rotate or leak inside Databricks.
For the permission, READ FILES on the volume is exactly right. It allows users to read and list files through the volume path while blocking writes, deletes, and modifications. This is the minimum necessary access, honouring the principle of least privilege.
WRITE FILES would allow modifications, contradicting 'users can access but NOT modify.' ALL PRIVILEGES grants far more than needed. Service principals with stored client secrets would mean credentials inside Databricks, violating the 'does not require storing credentials' requirement.
Reference: https://learn.microsoft.com/en-us/azure/databricks/connect/unity-catalog/volumes
NEW QUESTION # 49
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: C
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 # 50
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 # 51
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to ingest data from CSV files stored in Azure Data Lake Storage Gen2. New rows are appended frequently.
You need to implement a data ingestion solution that meets the following requirements:
* New data must be available in near-real-time (NRT).
* The data must be stored in managed Delta tables.
* The solution must minimize custom code and maintenance effort.
What should you include in the solution?
Answer: B
Explanation:
Auto Loader incrementally detects and processes new files arriving in Azure Data Lake Storage Gen2 through the cloudFiles Structured Streaming source. It supports near-real-time ingestion while automatically tracking processed files, reducing the custom state-management code required. Its output can be written to a managed Delta table, and built-in schema inference and evolution reduce ongoing maintenance. Scheduled Spark batch jobs introduce latency based on their schedule and usually require custom file-tracking logic. An external table over CSV files does not ingest the data into a managed Delta table. Azure Data Factory can orchestrate ingestion, but it introduces another service and more configuration than the native Databricks capability needed here. Auto Loader is therefore the most direct and maintainable solution for continuously arriving cloud files. Microsoft Learn
Topic 1, Contoso Case Study
Overview
Contoso has a single Azure Databricks workspace named Workspace1 in the West US Azure region.
Workspace1 is enabled for Unity Catalog.
Workspace1 contains all-purpose clusters for both development and production workloads.
The company ' s Azure environment contains:
* In the West US, Central US, and East US Azure regions, Azure event hubs that stream telemetry data and an Azure Data Lake Storage Gen2 account in each region for each hub
* A single Azure SQL database in the West US region that hosts enterprise resource planning (ERP) data
* An Azure Database for PostgreSQL server in the West US region that stores operational maintenance data Company information Contoso, Inc. is a renewable energy provider that operates solar and wind farms across North America.
Data Environment
Contoso ingests the following operational and business data:
* Telemetry data: More than 40,000 loT sensors across 28 sites emit JSON telemetry events every few seconds. Each site sends the events to the nearest event hub, which writes the data into the corresponding Data Lake Storage Gen2 account. These files frequently experience schema drift.
* Maintenance logs: Maintenance systems generate historical repair logs, daily incremental updates, technician notes, and unstructured attachments that are stored in the Data Lake Storage Gen2 accounts.
* Operational maintenance data: Structured operational maintenance data is stored on the Azure Database for PostgreSQL server.
* External weather data: Hourly weather forecasts are retrieved from a REST API and written to the Data Lake Storage Gen2 accounts.
* ERP data: Daily CSV extracts of 50 to 100 GB contain equipment metadata, work orders, and purchase order information.
Problem Statements
The company ' s existing analytics environment has several issues:
Ingestion
* Telemetry pipelines fall behind during peak loads.
* Telemetry ingestion fails when schema drift occurs.
* Streaming pipelines reprocess events after a pipeline restarts.
Compute
* Production and development workloads run on the same all-purpose clusters.
* Production and development workloads do NOT support autoscaling or workload isolation.
Governance
* The ERP data is duplicated across systems and development teams.
* Naming conventions are inconsistent across development teams, regions, and products.
* Ownership of the loT sensors changes over time, and analysts must track the full history of the ownership.
* Occasionally, equipment manufacturers must correct data-entry mistakes in equipment names. Historical values are NOT required.
Pipeline operations
* Pipelines lack resiliency, alerting, and centralized scheduling.
Planned Changes
Contoso plans to implement the following changes:
* Implement scalable data pipeline orchestration.
* Create a managed analytics catalog in Unity Catalog.
* Implement a consistent approach to creating curated datasets.
* Establish a centralized governance model across ingestion, cleansed, and curated layers.
* Grant data engineers access to the ERP tables by using minimal development effort.
* Adopt a compute strategy that isolates production workloads and supports autoscaling.
* Adopt a slowly changing dimension (SCD) approach to address current data modeling issues.
Technical Requirements
Contoso identifies the following environment and compute requirements:
* Ensure that production ingestion workloads run on compute clusters that can scale automatically during telemetry spikes.
* Provide fast and consistent performance for business intelligence (Bl) workloads.
* Prevent development activity from affecting production pipelines.
* Production ingestion workloads must run as scheduled, non-interactive pipelines rather than on shared interactive development clusters.
Contoso identifies the following data ingestion and processing requirements:
* Auto-scale ingestion pipelines to handle bursty workloads.
* Handle schema drift for the maintenance and telemetry data.
* Ingest file-based telemetry data by using minimal operational effort.
* Store all the ingested data in a format that supports incremental processing.
* Support the continuous ingestion of telemetry data from the event hubs by using exactly-once semantics.
* Support the ingestion of the structured maintenance data from the Azure Database for PostgreSQL server.
* Build a new telemetry pipeline that ingests raw events from the event hubs, cleanses the data, and publishes curated tables to Unity Catalog.
* Ensure that the Apache Spark Structured Streaming pipelines reading from the event hubs write the data into a managed Delta table named telemetry.raw_events. The pipelines must support schema drift and resume processing after failures without reprocessing the data.
Contoso identifies the following data modeling and optimization requirements:
* Build curated tables that standardize business logic.
* Overwrite equipment metadata attributes, such as name, manufacturer, model, and commissioning date, when the attributes change. Historical values are NOT required.
Contoso identifies the following pipeline deployment and operation requirements: |
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