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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure and govern Unity Catalog objects | 15–20% | - Implement data governance and security
|
| Topic 2: Set up and configure an Azure Databricks environment | 15–20% | - Select and configure compute resources
|
| Topic 3: Deploy and maintain data pipelines and workloads | 30–35% | - Monitor, troubleshoot, and maintain workloads
|
| Topic 4: Prepare and process data | 30–35% | - Ingest and transform data
|
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NEW QUESTION # 66
You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1.
metastore1 contains:
* A catalog named Sales
* A schema named Customers in the Sales catalog
* A table named Customer_details in the Customers schema
You need to ensure that a user named User1 can update the data in Customer_details. The solution must meet the following requirements:
* Ensure that User1 cannot create new tables.
* Follow the principle of least privilege.
Which permission should you grant to User1 for each object? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
User1 requires permissions at each level of the Unity Catalog hierarchy. USE CATALOG allows User1 to interact with objects contained in the Sales catalog. USE SCHEMA permits access to objects within the Customers schema. Neither permission independently grants access to the data.
At the table level, MODIFY permits User1 to insert, update, and delete data in Customer_details. It does not grant permission to create new tables. The CREATE TABLE privilege is deliberately not granted on the Customers schema, ensuring that User1 cannot create tables. MANAGE would provide unnecessarily broad administrative capabilities, including the ability to manage permissions, and would violate the principle of least privilege. SELECT provides read access but does not authorize updates. Azure Databricks Therefore, the minimum permissions available in the answer area are USE CATALOG on Sales, USE SCHEMA on Customers, and MODIFY on Customer_details.
NEW QUESTION # 67
You need to deploy Databricks Asset Bundles to a development environment. The solution must support automated and repeatable deployments across environments. What should you use?
Answer: A
Explanation:
The best option to use is the Databricks CLI.
The Databricks CLI is the core orchestration tool natively designed to validate, deploy, and manage Declarative Automation Bundles (formerly known as Databricks Asset Bundles) across multiple target environments using simple terminal commands like databricks bundle deploy.
Incorrect:
[Not A]
Git folders: Git folders are strictly a mechanism for version control, code syncing, and repository collaboration inside the workspace. While they are highly integrated with bundle development, they cannot directly deploy or provision bundle targets to isolated environments on their own without the CLI.
[Not C]
Databricks SDK for Python: While the Python SDK provides general programmatic access to Databricks APIs, it is not the primary or optimized tool for executing bundle operations. The bundle framework natively relies on the Databricks CLI executable to compile and translate your configurations.
[Not D]
Jobs UI: The Jobs user interface is built for manual interactions. It does not support native bundle multi-environment targeting or automated, repeatable CI/CD pipelines.
Reference:
https://docs.databricks.com/aws/en/dev-tools/bundles/
NEW QUESTION # 68
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 Databricks access connector.
Does this meet the goal?
Answer: B
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:
1. Create a Connection
Create a securable object in Unity Catalog that specifies the path and credentials to access the SQL Server database.
Go to Catalog Explorer or use SQL.
Select External Data > Connections.
Create a connection using the SQL Server connection details (URL, host, port, and database credentials).
*-> 2. Create a Foreign Catalog
Create a specific type of catalog in Unity Catalog that mirrors the external database.
Use the CREATE FOREIGN CATALOG SQL command or the Catalog Explorer UI.
Link this foreign catalog directly to the connection you created in step 1.
3. Query the DataOnce the foreign catalog is created, Unity Catalog automatically syncs the schemas and tables from SQL Server.
Reference:
https://docs.databricks.com/gcp/en/database-objects/
NEW QUESTION # 69
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Payments.
Payments stores transaction data and contains a column named payment_amount of the Decimal data type.
You must enforce the following business rule:
payment_amount must be between 0 and 10,000, inclusive
You need to ensure that records that violate the rule are rejected when data is written to the Payments table.
What should you do?
Answer: A
Explanation:
A CHECK constraint enforces a Boolean condition whenever data is inserted or updated. The constraint can require payment_amount > = 0 AND payment_amount < = 10000, causing a transaction containing an invalid value to fail instead of allowing the record into Payments. This provides storage-level data-quality enforcement regardless of which pipeline, notebook, or SQL statement performs the write. Row-level security controls which existing records users can see; it does not reject invalid writes. SELECT statements filter results only when they are executed and therefore cannot protect the underlying table. Table update triggers are not the standard Delta Lake mechanism for this requirement. Azure Databricks classifies CHECK constraints as enforced constraints and rejects transactions when their conditions are violated. Microsoft Learn
NEW QUESTION # 70
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Sales_orders.
Sales_orders stores historical sales data.
You receive a daily CSV file daily that contains new sales records only. The file does NOT contain updates to existing rows.
You need to load the daily data into Sales_orders. The solution must meet the following requirements:
- Preserve the existing data.
- Add only the new records.
- Minimize processing effort.
Which command should include in the loading strategy?
Answer: B
Explanation:
The best command for this scenario is INSERT INTO.
The INSERT INTO command appends new rows directly to the end of an existing Delta table.
Because your daily CSV file contains only new records and zero updates to existing rows, simply appending the data completely satisfies your requirements. It leaves all historical data untouched and requires the absolute lowest processing power because Databricks does not need to scan, modify, or rewrite any existing files.
Incorrect:
The UPDATE command is used to modify values in rows that already exist in the table based on a matching condition. It cannot be used to add entirely new rows to a table, and it requires a heavy scan of the data to find matches, which violates the requirement to keep processing low.
The INSERT OVERWRITE command deletes all the existing historical data in the table (or a specific partition) and replaces it with the data from the new CSV file. Using this would erase your historical sales data.
Reference:
https://medium.com/@jithujosekokken/understanding-data-patterns-in-medallion-architecture-full- incremental-and-change-only-loads-e33db28e51f4
NEW QUESTION # 71
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