You have an Azure Databricks workspace.
Users report that a Databricks notebook that runs each day takes longer than expected to run.
When reading the Directed Acyclic Graph (DAG), you discover the following issues concerning the Apache Spark stage:
* Most tasks in the stage finish quickly.
* A few tasks in the stage run more slowly.
* The CPU is underutilized at the end of the stage.
* The slow tasks process many more input records.
* The stage is blocked while it waits for the few slow tasks.
What is the root cause of the issues?

• A. caching
• B. spilling
• C. skewing
• D. shuffling

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Which SCD type should you use to support the planned data modeling changes? To answer, drag the appropriate types to the correct issues. Each type 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.

Explanation:
The correct mapping is SCD Type 1 for equipment metadata and SCD Type 2 for IoT sensor ownership history.
SCD Type 1 overwrites the existing record whenever an attribute changes - no history is kept. Contoso ' s requirement for equipment metadata (name, manufacturer, model, commissioning date) states ' historical values are NOT required, ' which is the textbook definition of Type 1. A MERGE INTO with WHEN MATCHED THEN UPDATE handles this cleanly in Delta Lake.
SCD Type 2 creates a new row for each change, preserving the full history through effective-date or version columns. Contoso requires that ' analysts must track the full history of ownership ' as sensors change hands over time - that full audit trail is only possible with Type 2. Type 3 (keeping just the previous value in an extra column) would lose earlier ownership records, so it doesn ' t satisfy the ' full history ' requirement.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/merge

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.

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

You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster! You need to configure Cluster1 to meet the following requirements;
* The cluster must scale up automatically when workloads increase.
* The cluster must scale down automatically when workloads decrease.
The solution must minimize costs.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

• A. Apply a compute policy that enables users to manage the cluster settings.
• B. Configure Cluster1 to terminate after 30 minutes of inactivity.
• C. Enable autoscaling for Cluster1.
• D. Specify a fixed number of workers.
• E. Disable Photon acceleration.

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You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer data.
You need to implement a data retention solution that meets the following requirements:
Deleted data must be retained for 30 days to support audits.
Deleted data that is older than 30 days must be removed permanently.
The solution must minimize administrative effort.
Which two properties should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

• A. delta.logRetentionDuration
• B. delta.deletedFileRetentionDuration
• C. delta.autoOptimize.autoCompact
• D. delta.timeUntilArchived
• E. delta.enableDeletionVectors

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