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| Section | Weight | Objectives |
|---|---|---|
| Prepare and process data | 30-35% | - Data ingestion
|
| Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Deploy and manage data pipelines and workloads | 30-35% | - Lakehouse architecture operations
|
| Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
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NEW QUESTION # 36
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Sales.
Sales stores transaction data and contains the following columns:
- transaction_id (string)
- transaction_date (date)
- amount (decimal)
You need to implement the following data quality requirements by using table-level data quality enforcement:
- amount must be greater than 0.
- transaction_id must never be null.
- Invalid records must be rejected when data is written to the Sales
table.
What should you do?
Answer: B
Explanation:
To enforce these table-level data quality rules and automatically reject any invalid write operations in Azure Databricks, you must configure NOT NULL and CHECK constraints on the Delta table using standard SQL commands.
In Delta Lake, these constraints are enforced instantly on write. If an incoming record violates any of them, the entire transaction fails, and the invalid records are rejected.
1. Enforce transaction_id Cannot Be Null
Add a NOT NULL constraint to the transaction_id column. This blocks any write attempt containing a null identifier.
ALTER TABLE sales_data ALTER COLUMN transaction_id SET NOT NULL;
2. Enforce amount Must Be Greater Than 0
Add a CHECK constraint to the table to validate that the amount values strictly exceed zero.
ALTER TABLE sales_data ADD CONSTRAINT check_amount_positive CHECK (amount > 0); Reference:
https://docs.databricks.com/aws/en/tables/constraints
NEW QUESTION # 37
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 processes raw data files stored in Azure Storage.
New files arrive at unpredictable intervals.
You need to ensure that Job1 starts automatically when new files arrive and does NOT consume compute resources when no data is available.
Which type of job trigger should you use?
Answer: B
Explanation:
The File arrival trigger is exactly what you need for this use case. It natively solves your problem by launching a job the moment new files land in your Azure Storage container, automatically scaling down to zero compute when no data is processing.
Event-Driven: Instead of relying on a time-based schedule (cron) that wastes resources, the Lakeflow Job monitors the specific Azure location and starts exactly when new data appears.
Zero Idle Compute: You can use serverless compute or an ephemeral job cluster that spins up just for the duration of the run and terminates immediately after, meaning zero billing during downtime.
Cloud Cost Efficient: Listing files to detect new arrivals is typically free or fractions of a cent (only incurring minimal cloud provider API costs for listing the storage location).
Reference:
https://learn.microsoft.com/en-us/azure/databricks/jobs/file-arrival-triggers
NEW QUESTION # 38
You need to develop the task logic for a new job in Lakeflow Jobs that processes telemetry data.
Each task must contain only the appropriate logic for its step in the pipeline. The solution must support the planned changes and meet the data ingestion and processing requirements.
What should you do?
Answer: C
Explanation:
The correct answer is D. Breaking the pipeline into separate tasks for ingestion, cleansing, and curation is the foundation of well-designed Lakeflow Jobs pipelines. Each task should own one responsibility - when a task does too much, debugging a failure becomes a hunt through unrelated code, and retry logic becomes expensive because you re-execute work that already succeeded.
Contoso ' s planned changes explicitly call for ' a clear execution order and dependencies ' and ' orchestrate multi-step ingestion and transformation workflows. ' Separate tasks map directly to those goals: Lakeflow Jobs tracks each task ' s status independently, so if cleansing fails, ingestion doesn ' t re-run.
Option A bundles everything into one notebook, which means a curation bug forces a full re-ingestion. Option B copies logic three times - any future change must be applied in triplicate, which is a maintenance hazard.
Option C forces everything through SQL MERGE, which is the wrong tool for raw-event ingestion and doesn
' t address cleansing or schema drift.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/
NEW QUESTION # 39
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: B
Explanation:
The correct answer is C - the Databricks CLI.
Databricks Asset Bundles are deployed using the Databricks CLI (v0.205+) with the commands databricks bundle validate, databricks bundle deploy, and databricks bundle run. The CLI reads the databricks.yml file, resolves the target environment settings, and creates or updates all declared resources (jobs, pipelines, apps) in the workspace. This is the documented, supported deployment mechanism for DABs and integrates naturally into CI/CD pipelines.
Option A (Azure Developer CLI / azd) is a general Azure IaC tool with no native understanding of Databricks bundles. Option B (Git folders) syncs notebook and file content from a Git repository into a workspace - useful for code, but it doesn't deploy Lakeflow Jobs, SDP pipelines, or bundle configurations. Option D (Azure CLI) manages Azure infrastructure (resource groups, storage accounts, etc.) and has no native DAB deployment support.
Reference: https://learn.microsoft.com/en-us/azure/databricks/dev-tools/bundles/deploy-bundle
NEW QUESTION # 40
You have an Azure Databricks workspace that contains the objects shown in the following table.
Name | Type
Catalog1 | Catalog
Schema1 | Schema
Sales1 | Table
Notebook1 | Notebook
Space1 | AI/BI Genie space
Users often use the following words to refer to a sale: transaction, event, order, and invoice.
You need to create a knowledge store. The solution must ensure that when the users use any of the words in Space1, Genie queries the Sales1 table. Any other Genie spaces must remain unaffected.
To which object should you add the instructions?
Answer: A
Explanation:
The instructions must be added to Space1 because a Genie knowledge store is scoped to the individual Genie space, now called a Genie Agent. Adding synonyms and business-language instructions there teaches Space1 that "transaction," "event," "order," and "invoice" refer to sales information in Sales1. The configuration affects only that Genie space, satisfying the requirement that other spaces remain unchanged. Adding instructions to Sales1 or Schema1 would modify shared Unity Catalog metadata and could affect other consumers of those objects. Notebook1 is unrelated to the semantic instructions used by Genie when converting natural-language questions into SQL. Genie knowledge stores contain space-specific definitions, synonyms, join relationships, SQL expressions, and prompt-matching guidance without changing the underlying Unity Catalog objects. Microsoft Learn
NEW QUESTION # 41
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