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| Section | Weight | Objectives |
|---|---|---|
| Deploy and manage data pipelines and workloads | 30-35% | - Operational reliability
|
| Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Prepare and process data | 30-35% | - Data ingestion
|
| Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
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NEW QUESTION # 49
Which feature helps reduce data scan during query execution in Delta Lake?
Answer: B
Explanation:
Delta Lake uses data skipping based on file-level statistics (min/max values). This reduces unnecessary file scans and improves query performance. VACUUM removes old files but does not improve query speed. Cluster restart has no impact on query optimization.
NEW QUESTION # 50
You need to configure compute for the ingestion of telemetry data. The solution must meet the data ingestion and processing requirements.
What should you do?
Answer: C
Explanation:
The correct answer is A. Photon is Azure Databricks' native vectorized query engine, written in C++, designed to accelerate data ingestion and SQL-heavy workloads significantly over the standard Spark JVM path. Enabling it on a job compute cluster directly addresses Contoso's requirement for 'fast and consistent performance for BI workloads' and 'production ingestion workloads that can scale automatically during telemetry spikes.' Photon integrates transparently - no code changes are needed - and pairs well with autoscaling job clusters to handle the bursty 40,000-sensor telemetry load.
Option B contradicts the isolation requirement: Contoso explicitly needs production and development separated, not merged onto shared compute. Option C with a fixed large node gives peak capacity at all times, driving up costs even during quiet periods. Option D disabling autoscaling is the opposite of what's needed - telemetry spikes require elastic scaling, not a locked node count.
Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/photon
NEW QUESTION # 51
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?
Answer: D
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:
1. Create the masking SQL UDFs
Define functions to apply the exact partial masking rules requested.
2. Apply the column masks to the tableAlter the transaction table to attach these UDFs to the respective columns.
3. Grant least privilege permissionsGrant the group the SELECT permission on the table, along with the foundational permissions to access the catalog and schema.
Reference:
https://docs.databricks.com/aws/en/data-governance/unity-catalog/filters-and-masks
NEW QUESTION # 52
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to share curated data with an external organization. The solution must meet the following requirements:
* The organization will use its own compute platform to query the data.
* Access to the data must be centrally governed by using Unity Catalog.
* Administrative effort must be minimized.
What should you do?
Answer: D
Explanation:
Delta Sharing is designed to share governed data securely with recipients outside an Azure Databricks workspace. The external organization can query the shared data from its own compatible compute platform without receiving workspace access or requiring a Databricks SQL warehouse. Unity Catalog centrally controls which tables, views, or other objects are included in the share and which recipients can access them.
Moving files to an SFTP server creates additional copies and requires custom transfer and security administration. Lakeflow Connect is intended for ingesting data into Databricks rather than sharing curated data externally. Granting workspace access or creating a SQL warehouse would require the recipient to use Databricks-managed resources. Delta Sharing therefore provides the required open access model, centralized governance, and minimal administrative effort.
NEW QUESTION # 53
Which component enforces table-level permissions in Databricks?
Answer: C
Explanation:
Unity Catalog provides fine-grained access control at table, schema, and column levels. It centralizes governance across workspaces. Cluster policies control compute settings. Spark configuration does not manage security. DBFS permissions are not sufficient for enterprise governance.
NEW QUESTION # 54
......
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