Online Access Free DP-750 Exam Questions

You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! runs every hour.
Occasionally, the job run takes longer than one hour to complete. Overlapping runs must be prevented to avoid data corruption.
You need to configure the job scheduling behavior.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
Two settings address the overlapping-run problem:
Concurrent Runs policy set to ' Skip ' (or ' Allow only one concurrent run ' ). When a new scheduled trigger fires while the previous run is still in progress, the new run is skipped rather than starting alongside the ongoing one. This prevents two runs from writing to the same tables at the same time - which is the data corruption risk the question highlights.
Cron-based schedule for the hourly trigger. A cron expression defines the regular execution cadence.
Combined with the concurrency setting, the job runs hourly but never overlaps.
An alternative to ' Skip ' is ' Wait ' (queue the new run), which ensures every scheduled run eventually executes - but for this scenario where overlapping is the primary concern, skipping the missed run is typically preferable to building up a queue of back-to-back executions.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#concurrent-runs

You have an Azure Databticks workspace that contains an all-purpose compute cluster named Cluster1.
Cluser1 is used for
interactive development.
You need to configure Cluster1 to meet the following requirements:
* Automatically add and remove worker nodes based on workload demand
* Automatically shut down when the cluster has been idle for a specific period.
What should you configure for each requirement? To answer, drag the appropriate options to the correct requirements. Each option may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
Two separate cluster settings address the two requirements:
Autoscaling handles the first requirement - dynamically adding workers when the workload is heavy and removing them when it lightens. You set a minimum and maximum node count, and Databricks adjusts the cluster size between those bounds based on task queue depth.
Auto-termination handles the second - the cluster shuts itself down after a configurable idle period (e.g., 30 minutes with no active queries), preventing wasted spend on a development cluster left running overnight.
These two settings are independent and complementary: autoscaling manages horizontal elasticity during active use, while auto-termination manages complete shutdown during inactivity. Both are configured in the cluster creation UI under the Compute section.
Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/configure#autoscaling

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?

• A.the Azure Developer CLI (azd)
• B.the Databricks CLI
• C.the Azure Command-Line Interface (CLI)
• D.Git folders

You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1.
Metastore1 contains a catalog named catalog 1.
You need to create a new schema named schema2 that meets the following requirements:
* Is contained in catalog1
* Uses abfss://containergstorageaccount.dfs.core.windows.net/data as the Managed location Which SQL statement should you execute?

• A.CREATE SCHEMA catalog1.schema2
  MANAGED LOCATION ' abfss://[email protected]/data ' ;
• B.CREATE SCHEMA catalog1.schema2
  LOCATION ' abfss://[email protected]/data ' ;
• C.CREATE CATALOG schema2
  MANAGED LOCATION ' abfss://[email protected]/data ' ;
• D.CREATE SCHEMA catalog1.schema2
  WITH DBPROPERTIES (LOCATION= ' abfss://[email protected]/data
  ' );

You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to create a job in Lakeflow Jobs named Job1 that:
* Ingests data from cloud storage
* Runs two independent transformation tasks
The transformation tasks must run only after the ingestion completes and must run in parallel.
You need to design the task logic for Job1.
What should you configure?

• A.two ingestion tasks, each followed by a transformation task
• B.independent tasks with no defined dependencies
• C.One ingestion task with two parallel downstream transformation tasks
• D.a single task that performs ingestion and transformations sequentially