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| Section | Weight | Objectives |
|---|---|---|
| Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
| Prepare and process data | 30-35% | - Ingest and transform data
|
| Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
| Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
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NEW QUESTION # 44
You have an Azure Databricks workspace.
You have an Azure key vault named kv-secure that stores a secret named storageKey. The value of storageKey is managed and updated by the cloud security team at your company.
You need to enable a Databricks notebook named Notebook 1 to retrieve the value of storageKey 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.
Answer:
Explanation:
Explanation:
The solution is a Key Vault-backed secret scope combined with dbutils.secrets.get() in the notebook.
An Azure Key Vault-backed secret scope does not copy the secret value into Databricks. Every call to dbutils.
secrets.get() fetches the live current value directly from Key Vault at runtime. This means when the cloud security team rotates storageKey in Key Vault, the notebook automatically picks up the new value on its next run - no Databricks configuration changes needed. That satisfies 'always retrieve the latest value.' The principle of least privilege is upheld because only the secret scope has read access to Key Vault through a service principal or managed identity, and individual users never see the raw secret value - Databricks redacts it in logs automatically.
A Databricks-backed scope stores a copy of the secret inside Databricks, so it wouldn't automatically reflect Key Vault updates. Hardcoding or using environment variables would expose the secret in plain text.
Reference: https://learn.microsoft.com/en-us/azure/databricks/security/secrets/secret-scopes
NEW QUESTION # 45
Hotspot Question
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to run the following PySpark code.
Answer:
Explanation:
NEW QUESTION # 46
You need to recommend a compute type for the production ingestion workloads and BI workloads. The solution must meet the environment and compute requirements.
What should you recommend for each type of workload? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Production ingestion: Job compute
BI: Serverless SQL warehouse
Job compute is designed for automated production workloads executed through Lakeflow Jobs. Its lifecycle can be tied to the job run, providing workload isolation and avoiding the cost of maintaining an interactive all- purpose cluster continuously. It is therefore appropriate for scheduled ingestion and transformation processing. A serverless SQL warehouse is designed for BI and Databricks SQL workloads. It provides rapid startup, automatic infrastructure management, scaling, and optimized SQL-query execution for dashboards and reporting tools. All-purpose compute is intended primarily for interactive notebook development and exploration, while shared compute does not provide the same job-specific isolation or SQL-serving experience. Consequently, job compute should support production ingestion, and a serverless SQL warehouse should serve the BI workload.
NEW QUESTION # 47
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains:
A catalog named Corpdb
A schema named Finance in the Corpdb catalog
A table named Sales in the Finance schema
You have a group named Analysts.
You assign the following permissions to Analysts:
USE CATALOG on the Corpdb catalog
USE SCHEMA on the Finance schema
SELECT on the Sales table
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The Analysts group can query the Sales table because it has all three privileges required by the Unity Catalog hierarchy: USE CATALOG on Corpdb, USE SCHEMA on Finance, and SELECT on Sales. These permissions allow the group to navigate through the catalog and schema and read the table. However, USE SCHEMA does not grant permission to create tables. Creating a table in Finance would additionally require CREATE TABLE on that schema. Similarly, USE CATALOG only permits access to the catalog; it does not permit schema creation. Creating a schema in Corpdb would require the CREATE SCHEMA privilege on the catalog. Therefore, only the first statement is true.
NEW QUESTION # 48
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?
Answer: D
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
NEW QUESTION # 49
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