Microsoft DP-750 시험

Question No : 1

Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have an Azure Databricks workspace named Workspace1 that contains a lakehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
- The schemas and tables can be queried in Databricks.
- The schemas and tables appear alongside other Unity Catalog objects.
- The data is NOT copied into Databricks-managed storage.
Solution: You create a Lakeflow Connect pipeline and connect it to DB1.
Does this meet the goal?

• A.Yes
• B.No

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Explanation:
Correct:
* You create a foreign catalog in Catalog Explorer.
You should create a Foreign Catalog using Lakehouse Federation.
Data Copying: Lakehouse Federation queries data directly in the source SQL Server without moving or copying it.
Seamless Integration: The database schemas and tables appear right inside Unity Catalog alongside your other data objects. Real-time Access: It provides immediate access to live SQL Server data.
Incorrect:
* You create a Databricks access connector.
* You create a Lakeflow Connect pipeline and connect it to DB1.
Data Copying: Lakeflow Connect is an ingestion tool that physically replicates and copies data into Databricks-managed storage (Delta tables).
Storage Costs: It violates your requirement to keep data out of Databricks storage. * You create a new native catalog in Unity Catalog. Note:
To expose the external SQL Server database in Unity Catalog without copying the data, you must use Lakehouse Federation.
Here are the step-by-step actions you need to take:

Question No : 2

HOTSPOT
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.

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Explanation:
Box 1: A Databricks access connector
The correct authentication type to use is the Databricks access connector.
To comply with the requirements, you should use an Azure managed identity associated with a Databricks Access Connector. This approach is recommended by Databricks because it avoids the need to store and rotate long-term credentials (like service principal secrets or SAS tokens) within the workspace.
Authentication Mechanism: The Databricks access connector acts as a container for a managed identity. This identity is granted the necessary Azure RBAC permissions (such as Storage Blob Data Reader) on the Azure Storage container.
Unity Catalog Setup:
Create a Storage Credential in Unity Catalog that references the Access Connector's resource ID.
Create an External Location that links the cloud storage path (the container URL) to that Storage Credential.
Box 2: READ VOLUME
Determine the Databricks Permission
To allow users to view and interact with files inside the external volume but prevent them from making any edits, you must grant them specific Unity Catalog object permissions.
Permission to grant: READ VOLUME
Scope: This allows the designated users or groups to list, read, and process files within that volume. Because you are omitting the WRITE VOLUME permission, users will be structurally blocked from uploading, altering, or removing files inside the volume.
Reference: https://docs.databricks.com/aws/en/volumes/privileges

Question No : 3

HOTSPOT
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named catalog1.
You have a group named group1.
You plan to create a schema named schema1 in catalog1.
You need to ensure that group1 meets the following requirements:
- Can create tables in schema1
- Can modify and query tables
- Cannot grant permissions for the schema and its objects
How should you complete the SQL statements? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Explanation:
Box 1: USAGE, CREATE TABLE
USAGE, CREATE TABLE allows entering the schema and creating new tables.
Box 2: TO ROLE group1
Reference: https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/access-control/privileges-reference

Question No : 4

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named Catalog1. Catalog1 contains a table named Transactions.
Transactions contains the following columns:
- transaction_id
- customer_name
- email_address
- credit_card_number
- transaction_amount
You need to ensure that business analysts can query all the rows in the Transactions table.
The solution must meet the following requirements:
- Prevent the analysts from seeing the full values in the email_address and credit_card_number columns.
- Ensure that the analysts can see only the values after the @ character in each email address.
- Ensure that the analysts can see only the last four digits of each credit card number.
- Enable the analysts to query the table without errors.
- Follow the principle of least privilege.
What should you do?

• A.Grant the analysts the SELECT permission for the Transactions table and apply column-level encryption.
• B.Grant the analysts the SELECT permission for columns that do NOT contain sensitive data.
• C.Grant the analysts the SELECT permission for the Transactions table and implement row-level filters.
• D.Grant the analysts the SELECT permission for the Transactions table and apply column masks to email_address and credit_card_number.

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Explanation:
To protect sensitive customer information while allowing a specific group to run queries, use Unity Catalog's Dynamic Column Masking. This grants the group table access while applying User-Defined Functions (UDFs) to redact the email addresses and credit card numbers dynamically.
Here is the exact SQL implementation to apply:

Question No : 5

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two catalogs
named Catalog1 and Catalog2.
An external application uses a service principal named SP1 to connect to a SQL warehouse.
You need to ensure that SP1 can query the data in Catalog1 and Catalog2. The solution must follow the principle of least privilege.
Which permissions should you grant to SP1 for the catalogs?

• A.USE SCHEMA and SELECT
• B.USE CATALOG and SELECT
• C.USE CATALOG, USE SCHEMA, and SELECT
• D.USE CATALOG and USE SCHEMA

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Explanation:
To ensure a service principal can query data in two specific catalogs while maintaining the principle of least privilege, you must assign the following permissions for each catalog:
Unified Catalog Permissions
USE CATALOG on the specific catalog: This is a prerequisite that allows the principal to see the catalog and its child objects.
USE SCHEMA on the schemas containing the data: This allows the principal to interact with the schemas within that catalog.
SELECT on the specific tables or views: This provides the actual read access required to query the data.
Reference: https://learn.microsoft.com/en-us/purview/data-governance-roles-permissions

Question No : 6

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

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Explanation:
To configure an Azure Databricks managed Delta table to retain deleted data for 30 days and minimize administrative overhead, you must set the following table properties:
delta.logRetentionDuration: Set this to interval 30 days. This property controls how long the transaction log history is kept, which is essential for audit trails and time travel.delta.
deletedFileRetentionDuration: Set this to interval 30 days. This property determines the threshold for when deleted data files become eligible for permanent removal by the VACUUM command.
Reference: https://docs.databricks.com/aws/en/delta/history

Question No : 7

HOTSPOT
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a data lifecycle and expiration solution that meets the following requirements:
- Transaction logs and deleted data files that are older than 90 days must be removed from Delta tables to reclaim storage.
- All the tables must remain available for querying during the cleanup process.
- Administrative effort must be minimized.
What should you do for each requirement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Explanation:
Box 1: Set deletedFileRetentionDuration to 90.
The most important property to set to 90 is deletedFileRetentionDuration.
The delta.deletedFileRetentionDuration table property explicitly dictates the safety threshold used by the VACUUM command. Setting this to 90 days ensures that VACUUM will physically remove data files that have been logically deleted or unreferenced in the transaction log for more than 90 days to reclaim storage.
Incorrect:
UntilArchived
This is not a Delta table retention property. It is a conceptual flag or cloud lifecycle strategy but does not directly govern the behavior of the VACUUM command.
logRetentionDuration
The delta.logRetentionDuration property controls how long the table history and transaction log entries are kept, which defaults to 30 days. While you should ideally keep it equal to or greater than the deleted file retention (e.g., matching it at 90 days), it governs metadata cleanup rather than the physical removal of unreferenced data files targeted by the VACUUM command.
Box 2: Run the VACUUM command.
Execute the VACUUM table_name command. This deletes the actual physical files from your cloud storage to reclaim space. Tables remain online and fully queryable during this process.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-vacuum

Question No : 8

HOTSPOT
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named Catalog1. Catalog1 contains a schema named Schema1 and a table named Table1.
You need to ensure that access to the data in Table1 is controlled by using attribute-based access control (ABAC).
What should you apply to Table1, and how should you control access for users? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Explanation:
Box 1: A governed tag
You must apply governed tags (key-value pairs) to the table or its columns. These tags represent the attributes of the data, such as its sensitivity level or business domain.
Box 2: Create a Unity Catalog access policy
To control user access using attribute-based access control (ABAC) on a tagged table in Azure Databricks, you should create an ABAC policy (specifically a row filter policy or a column mask policy) that evaluates the governed tag applied to the table.
When configured, Unity Catalog dynamically maps the tag on the table against your policy rules to determine which users can view or mask specific rows and columns.

Question No : 9

HOTSPOT
You have an Azure Databricks workspace.
You have an Azure key vault named kv-secure that stores a secret named storage Key. The value of storage Key is managed and updated by the cloud security team at your company.
You need to enable a Databricks notebook named Notebook1 to retrieve the value of storage Key securely at runtime. The solution must follow the principle of least privilege and always retrieve the latest value.
What should you do? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Explanation:
Box 1: A Key Vault-backed secret scope
To securely expose the storage Key to your Databricks notebook while adhering to the principle of least privilege, use an Azure Key Vault-backed secret scope.
This approach acts as a read-only bridge to your Key Vault, ensuring the security team manages the key centrally, while the notebook always fetches the latest version dynamically.
Secret Exposure: Notebooks reference the secret by an alias name.
Always Updated: Databricks fetches the secret directly from Key Vault at runtime, ensuring it always gets the latest version updated by your security team.
Least Privilege: Access can be restricted to specific users or groups using Databricks Access Control Lists (ACLs).
Box 2: dbutils.secrets.get
Use an Azure Key Vault-backed secret scope in combination with the dbutils.secrets.get function.
This approach centralizes the secret's management for your security team, follows the principle of least privilege through Azure IAM, and ensures your notebook always fetches the latest runtime value.
Run this specific command inside your Databricks Python notebook:
storage_key = dbutils.secrets.get(scope="your-scope-name", key="storageKey")
Reference: https://medium.com/@techgeorge/how-to-integrate-databricks-with-azure-key-vault-using-rbac-instead-of-access-policies-972e8c1c2971

Question No : 10

HOTSPOT
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to ensure that data lineage is captured and can be reviewed for tables accessed by Databricks notebooks and jobs. The solution must minimize administrative effort.
Which compute configuration should you use to capture the data lineage and what should you use to review the data lineage? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Explanation:
Box 1: A Serverless cluster
To capture automated data lineage via Unity Catalog for both Databricks notebooks and jobs with low administrative overhead, you should use Serverless compute (or Shared access mode clusters if serverless is unavailable).
Recommended Compute Options
Serverless Compute (Highly Recommended):
This option completely minimizes administrative overhead. It requires no infrastructure management, scales automatically, starts instantly, and captures lineage inherently for both Databricks Notebooks and Jobs.
Box 2: Catalog Explorer
To review data lineage in an Azure Databricks workspace enabled for Unity Catalog, you should use Catalog Explorer.
Because you are using Unity Catalog and Serverless compute, table-level and column-level data lineage is automatically captured for notebooks and jobs without any manual configuration or administrative overhead.
View Lineage in Catalog Explorer
You can visually explore how data flows between tables by navigating through the Azure Databricks user interface:
Open the Catalog icon in the sidebar.
Select the desired catalog, schema, and table.
Click the Lineage tab.
Click See lineage graph to view an interactive map of upstream and downstream dependencies.
Reference:
https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/best-practices
https://kanerika.com/blogs/databricks-data-lineage/

Question No : 11

DRAG DROP
You have an Azure Databricks workspace named Workspace1 that is attached to a Unity Catalog metastore named metastore1.
You need to register an Azure Storage account named account1 that has a hierarchical namespace enabled as an external location. The external location must use a managed identity to authenticate to account1 and the solution must follow the principle of least privilege.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

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Explanation:
To register an Azure Storage account (ADLS Gen2) as an external location using a managed identity and an Access Connector under the principle of least privilege, you must complete the following configuration workflow:
Step 1: Create a Databricks access Connector
Action 1: Create an Access Connector for Azure Databricks Navigate to the Azure Portal.
Create a new resource and search for Access Connector for Azure Databricks.
Configure it in the same Azure region as your ADLS Gen2 storage account.
Ensure the System-assigned managed identity status is turned On (or attach a specific user-assigned managed identity). Once deployed, navigate to its properties and copy the Resource ID.
Step 2: Assign Workspace1 the Storage Blob Data Contributor role for account1.
Action 2: Configure Least-Privilege IAM Roles on the Storage Account Navigate to your hierarchical namespace-enabled Azure Storage Account. Open Access Control (IAM) and click Add role assignment. *-> Assign the Storage Blob Data Contributor role.
Note on Least Privilege: To enforce true least privilege, avoid assigning this role at the entire storage account level. Instead, scope this role assignment specifically to the target container inside the storage account.
Select Managed identity as the assignee type and select your Access Connector.
Step 3: Register the access connector as a storage credential in metastore1
Action 3: The Databricks access connector must be registered in the metastore (via a Unity Catalog Storage Credential), not in the individual workspace.
Centralized Security: Unity Catalog manages security at the account/metastore level.
Cross-Workspace Access: Registering it in the metastore allows the credential to be safely shared across multiple workspaces attached to that same metastore.
Object Hierarchy: In Unity Catalog, an External Location relies on a Storage Credential. Both are metastore-level securable objects.
Reference: https://community.databricks.com/t5/data-governance/unity-catalog-error-creating-table-errorclass-invalid-state/td-p/10009

Question No : 12

HOTSPOT
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1.
Table1 is written by batch jobs every hour and is queried frequently by filtering two columns named Customerid and EventDate.
You expect Table1 to grow significantly over time.
The rows in Table1 are frequently updated and deleted to support compliance requests.
You need to keep query performance consistent as Table1 grows. The solution must minimize update and deletion effort.
What should you include in the solution? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Explanation:
Box 1: Liquid Clustering
To optimize query performance, implement:
To optimize query performance for a rapidly growing, frequently updated managed Delta table filtered by customer and date in Unity Catalog, you must implement Liquid Clustering on both the customer and date columns, while running regular OPTIMIZE and VACUUM maintenance.
Liquid Clustering replaces traditional partitioning and Z-Ordering, drastically improving performance for tables with high-frequency write, update, and delete workloads.
Box 2: Enable deletion vectors.
To optimize updates and deletions:
Write and Execution Strategies
Use Deletion Vectors: Ensure Deletion Vectors are enabled (default in Databricks Runtime 12.1+). They record deletes and updates in a separate lightweight file instead of rewriting the entire data file immediately.
Leverage MERGE INTO: Execute updates and deletions in a single atomic batch using the MERGE command. Ensure your ON search condition explicitly includes filters for both the customer and date columns to trigger file pruning.
Reference: https://blog.devgenius.io/liquid-clustering-in-databricks-4002fddbf0be
Secure and govern Unity Catalog objects
Question Set 1

Question No : 13

You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have an Apache Spark Structured Streaming job that writes data to a Delta table.
After the cluster restarts, the streaming job reprocesses previously ingested data.
You need to prevent the streaming job from reprocessing the data after the cluster restarts.
What should you do?

• A.Configure a checkpoint location for the streaming query.
• B.Increase the trigger interval of the streaming query.
• C.Enable change data feed (CDF) for the target table.
• D.Configure a watermark for the streaming query.

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Explanation:
To prevent your Apache Spark Structured Streaming job from reprocessing previously ingested data after a cluster restart, you must configure a streaming checkpoint directory.
Core Solution
Enable Checkpointing: Define the checkpointLocation option in your streaming write configuration.
Track Progress: Spark uses this directory to save the exact offset ranges of processed data.
Automatic Recovery: Upon restart, the engine reads the checkpoint and resumes precisely where it left off.
Implementation Example in python
# Configure the streaming write with a checkpoint path
(df.writeStream
.format("delta")
.outputMode("append")
.option("checkpointLocation", "/Volumes/catalog/schema/volume_name/checkpoints/job_name")
.toTable("catalog.schema.target_table"))
Reference: https://medium.com/@salah.uddin_75300/architecture-of-a-streaming-machine-learning-data-pipeline-042200c8e7ff

Question No : 14

You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster1.
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.Disable Photon acceleration.
• B.Enable autoscaling for Cluster1.
• C.Apply a compute policy that enables users to manage the cluster settings.
• D.Specify a fixed number of workers.
• E.Configure Cluster1 to terminate after 30 minutes of inactivity.

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Explanation:
Enabling autoscaling and setting a 30-minute auto-termination timeout is the correct configuration to meet the goals.
Autoscaling handles fluctuating workloads, while auto-termination prevents you from paying for idle compute resources.
Dynamic Scaling: Autoscaling automatically adds workers during high loads and removes them when demand drops.
Cost Control: The 30-minute termination window ensures the cluster shuts down completely if no jobs are running, stopping all compute charges.
Reference: https://community.databricks.com/t5/get-started-discussions/cluster-auto-termination-best-practices/td-p/75826

Question No : 15

DRAG DROP
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named finance, finance contains two schemas named default and procurement.
You need to create a table named assets in the procurement schema, assets must contain the following columns:
- asset_id
- asset_type
- asset_name
How should you complete the SQL statement? To answer, drag the appropriate values to the correct targets. Each value 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.

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Explanation:
Completed SQL Statements
USE CATALOG finance;
USE SCHEMA procurement;
CREATE TABLE assets (
asset_id INT,
asset_name STRING,
asset_type STRING
);
Box 1: CATALOG finance
CATALOG finance - the root container holding your data.
Box 2: SCHEMA procurement
SCHEMA procurement - the specific database/schema where the table must live.
Box 3: TABLE assets
TABLE assets - The name of the table you are creating.
Reference: https://docs.databricks.com/aws/en/catalogs/