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NEW QUESTION 1
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 new native catalog in Unity Catalog.
Does this meet the goal?

A.    Yes
B.    No

Answer: B
Explanation:
https://docs.databricks.com/gcp/en/database-objects/

NEW QUESTION 2
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders. You load the Orders table into an Apache Spark DataFrame named df. You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression: df.filter(df.order_amount != None)
Does this meet the goal?

A.    Yes
B.    No

Answer: B
Explanation:
https://www.geeksforgeeks.org/python/filter-pyspark-dataframe-columns-with-none-or-null-values/
https://learn.microsoft.com/en-us/azure/databricks/pyspark/reference/classes/dataframe/dropna

NEW QUESTION 3
You have an Azure Databricks workspace that uses serverless compute. You need to ingest data by using Lakeflow Jobs. New records must be processed as soon as they become available. Which type of job trigger should you use for the ingestion?

A.    manual
B.    file arrival
C.    scheduled
D.    continuous

Answer: D
Explanation:
The best trigger type for this scenario is the Continuous trigger.
– Immediate Processing: The Continuous trigger mode processes new records as soon as they arrive at the configured data sources. This matches your requirement to ingest and process records without waiting for an artificial time interval.
– Native Serverless Support: When paired with serverless compute, Lakeflow Jobs efficiently manage resources by automatically scaling up or down according to the real-time stream volume.
– Built-in Fault Tolerance: Continuous pipelines on Databricks automatically handle failures by retrying with an exponential backoff policy, keeping your automated ingestion operational 24/7 without manual intervention.
https://docs.databricks.com/aws/en/jobs/continuous

NEW QUESTION 4
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?

A.    Use a SELECT statement with WHERE conditions to validate the data before querying.
B.    Create a view that filters out rows where transaction_id is null or amount is less than or equal to 0.
C.    Add a NOT NULL constraint to transaction_id and a CHECK constraint to amount.
D.    Configure row-level security (RLS) where transaction_id is null or amount is less than or equal to 0.

Answer: C
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);
https://docs.databricks.com/aws/en/tables/constraints

NEW QUESTION 5
You have an Azure Databricks workspace that contains multiple all-purpose clusters. You discover that some clusters remain idle for long periods after users finish their work. You need to reduce compute costs without affecting active workloads. What should you do?

A.    Enable autoscaling.
B.    Convert the clusters into job clusters.
C.    Use spot instances.
D.    Configure automatic termination.

Answer: D
Explanation:
To reduce compute costs from idle clusters without impacting active workloads, you must configure Auto-Termination and use Cluster Policies. Core remedies:
– Auto-Termination: Set a strict inactivity timeout (e.g., 20-30 minutes) on all-purpose clusters to automatically shut them down when idle.
– Cluster Policies: Enforce maximum auto-termination limits across the workspace so users cannot disable or set excessively long idle timeouts.
– Single User Access Mode: Use this mode where possible, as it tracks idleness more accurately than Shared mode by monitoring the specific user’s activity.
https://medium.com/@sujathamudadla1213/databricks-lakehouse-platform-describe-how-clusters-are-terminated-and-the-impact-of-terminating-a-b6236689fd2e