Online Access Free DP-750 Practice Test

You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster1.
You discover that out of- memory (OOM) errors intermittently cause jobs running on Cluster1 to fail.
You need to identify the root cause of the failures by analyzing the runtime execution behavior. What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
Diagnosing OOM errors requires analysing actual runtime execution behaviour. The Spark UI is the primary tool - it ' s accessible from the cluster detail page and captures rich per-stage and per-task metrics without any extra setup.
In the Executors tab, look at storage memory used, execution memory used, memory spill to disk, and GC time per executor. An executor showing high memory spill is a strong indicator that it ' s processing more data than it can hold in memory - often caused by data skew, where one partition is far larger than the others.
The Stages tab shows task distribution - if one task in a stage is processing 10x more data than its peers, that
' s data skew causing memory pressure on that specific executor. Ganglia (available on older runtimes) provides node-level OS metrics like heap usage over time, which can confirm whether memory pressure is sustained or spiky.
These built-in tools give a complete picture of the root cause before making any configuration changes.
Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/monitor-cluster

You have an Azure Databricks workspace named Workspace1 that contains a cluster named Cluster1.
You need to enable a user named User1 to install a Maven package on all the nodes of Cluster1. The solution must follow the principle of least privilege.
What should you do?

• A.Assign User1 the Contributor role for the resource group that contains Workspace1.
• B.Grant User1 the CAN MANAGE permission for Cluster1.
• C.Assign User1 the workspace admins role for Workspace1.
• D.Grant User1 the CAN RESTART permission for Cluster1.

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named db1.sales_orders.
dbl sales_orders is updated nightly and has change data feed (CDF) enabled.
You need to ingest all the changes from the dbl.sales.ordets table, including inserts, updates, and deletes, into a downstream pipeline.
How should you complete the PsySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
When Change Data Feed (CDF) is enabled on a Delta table, reading the full change stream - inserts, updates, and deletes - requires this pattern:
spark.readStream.format( ' delta ' ).option( ' readChangeFeed ' , ' true ' ).table( ' db1.sales_orders ' ) The readChangeFeed option switches the reader from the default ' new rows only ' mode to a mode that returns all change events. Each row in the resulting DataFrame includes a _change_type column (insert, update_preimage, update_postimage, delete) so downstream processing can distinguish what happened to each record.
Without readChangeFeed = true, streaming a Delta table only surfaces newly appended rows. Deletes and updates are invisible, making it unsuitable for true CDC pipelines. The stream also supports startingVersion or startingTimestamp options to begin from a specific point in table history rather than the current moment.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/delta-change-data-feed

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 have an Azure Databricks workspace that contains a Git folder and uses Azure Repos as the Git provider.
From the main branch, you create a branch named Branch1. You commit changes to Branch1.
You need to incorporate the changes from Branch1 into main The solution must preserve the commit history in the repository. Which command should you run?

• A.pull
• B.Push
• C.merge
• D.rebase