NO.18 Hotspot Question
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.

NO.19 You have an Azure Databricks workspace named Workspace1 that contains a takehouse 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?

NO.20 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.

NO.21 You have an Azure Databricks workspace named Workspace1 that uses a Git repository. The repository contains a Databricks notebook named Notebook1.
From the main branch, you create a feature branch named Branch1 and commit changes to Notebook1. Another user commits changes to Notebook1 in main.
When you attempt to merge Branch1 into main, the merge fails due to conflicts.
You need to merge Branch1 into the main branch. The solution must ensure that Notebook1 includes all the changes from both the branches.
What should you do?

NO.22 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?

NO.23 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?

NO.24 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?

NO.25 You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster! You need to configure Cluster1 to meet the following requirements;
* The cluster must scale up automatically when workloads increase.
* The cluster must scale down automatically when workloads decrease.
The solution must minimize costs.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

NO.26 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?

NO.27 Case Study 1 – Contoso, Inc.
Overview
Company Information
Contoso, Inc. is a renewable energy provider that operates solar and wind farms across North America.
Existing Environment
Azure Environment
Contoso has a single Azure Databricks workspace named Workspace1 in the West US Azure region. Workspace1 is enabled for Unity Catalog.
Workspace1 contains all-purpose clusters for both development and production workloads.
The company’s Azure environment contains:
– In the West US, Central US, and East US Azure regions, Azure event hubs that stream telemetry data and an Azure Data Lake Storage Gen2 account in each region for each hub
– A single Azure SQL database in the West US region that hosts enterprise resource planning (ERP) data
– An Azure Database for PostgreSQL server in the West US region that stores operational maintenance data Data Environment Contoso ingests the following operational and business data:
– Telemetry data: More than 40,000 IoT sensors across 28 sites emit JSON telemetry events every few seconds. Each site sends the events to the nearest event hub, which writes the data into the corresponding Data Lake Storage Gen2 account. These files frequently experience schema drift.
– Maintenance logs: Maintenance systems generate historical repair logs, daily incremental updates, technician notes, and unstructured attachments that are stored in the Data Lake Storage Gen2 accounts.
– Operational maintenance data: Structured operational maintenance data is stored on the Azure Database for PostgreSQL server.
– External weather data: Hourly weather forecasts are retrieved from a REST API and written to the Data Lake Storage Gen2 accounts.
– ERP data: Daily CSV extracts of 50 to 100 GB contain equipment metadata, work orders, and purchase order information.
Problem Statements
The company’s existing analytics environment has several issues:
Ingestion
– Telemetry pipelines fall behind during peak loads.
– Telemetry ingestion fails when schema drift occurs.
– Streaming pipelines reprocess events after a pipeline restarts.
Compute
Production and development workloads run on the same all-purpose clusters.
Production and development workloads do NOT support autoscaling or workload isolation.
Governance
– The ERP data is duplicated across systems and development teams.
– Naming conventions are inconsistent across development teams, regions, and products.
– Ownership of the IoT sensors changes over time, and analysts must track the full history of the ownership.
– Occasionally, equipment manufacturers must correct data-entry mistakes in equipment names.
Historical values are NOT required.
Pipeline operations
– Pipelines lack resiliency, alerting, and centralized scheduling.
Requirements
Planned Changes
Contoso plans to implement the following changes:
– Implement scalable data pipeline orchestration.
– Create a managed analytics catalog in Unity Catalog.
– Implement a consistent approach to creating curated datasets.
– Establish a centralized governance model across ingestion, cleansed, and curated layers.
– Grant data engineers access to the ERP tables by using minimal development effort.
– Adopt a compute strategy that isolates production workloads and supports autoscaling.
– Adopt a slowly changing dimension (SCD) approach to address current data modeling issues.
Technical Requirements
Contoso identifies the following environment and compute requirements:
– Ensure that production ingestion workloads run on compute clusters that can scale automatically during telemetry spikes.
– Provide fast and consistent performance for business intelligence (BI) workloads.
– Prevent development activity from affecting production pipelines.
– Production ingestion workloads must run as scheduled, non-interactive pipelines rather than on shared interactive development clusters.
Contoso identifies the following data ingestion and processing requirements:
– Auto-scale ingestion pipelines to handle bursty workloads.
– Handle schema drift for the maintenance and telemetry data.
– Ingest file-based telemetry data by using minimal operational effort.
– Store all the ingested data in a format that supports incremental processing.
– Support the continuous ingestion of telemetry data from the event hubs by using exactly-once semantics.
– Support the ingestion of the structured maintenance data from the Azure Database for PostgreSQL server.
– Build a new telemetry pipeline that ingests raw events from the event hubs, cleanses the data, and publishes curated tables to Unity Catalog.
– Ensure that the Apache Spark Structured Streaming pipelines reading from the event hubs write the data into a managed Delta table named telemetry.raw_events. The pipelines must support schema drift and resume processing after failures without reprocessing the data.
Contoso identifies the following data modeling and optimization requirements:
– Build curated tables that standardize business logic.
– Overwrite equipment metadata attributes, such as name, manufacturer, model, and commissioning date, when the attributes change. Historical values are NOT required.
Contoso identifies the following pipeline deployment and operation requirements:
– Orchestrate multi-step ingestion and transformation workflows.
– Define a clear execution order and dependencies.
– Automatically retry failed steps and notify operators.
– Schedule ingestion and transformation workloads consistently.
Governance Requirements
Contoso identifies the following governance requirements:
– Centralize the metadata catalog.
– Provide isolated development areas that follow standard naming conventions.
– Establish a consistent structure for organizing raw, cleansed, and curated data.
– Provide a read-only mechanism to reference the ERP data through a foreign catalog.
Business Requirements
Contoso identifies the following business requirements:
– Improve ingestion reliability and reduce operational effort.
– Standardize data definitions across development teams.
Drag and Drop Question
Which ingestion option should you recommend for each data source? To answer, drag the appropriate options to the correct data sources. 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.

NO.28 Hotspot Question
You have an Azure Databricks job named Job1 that contains an ingestion task named Task1 and transformation task named Task2.
You need to ensure that if Task1 fails, the task retries automatically, and Task2 is prevented from running.
How should you configure Job1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

NO.29 Drag and Drop Question
You have an Azure Databricks workspace named Workspace1 that is attached to a Unity Catalog metastore named metastore1.
You need to register an Azure Storage account named account1 that has a hierarchical namespace enabled as an external location. The external location must use a managed identity to authenticate to account1 and the solution must follow the principle of least privilege.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

NO.30 You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1, metastore1 contains a catalog named catalog1.
You need to create a new schema named schema2 that meets the following requirements:
– Is contained in catalog1
– Uses abfss://[email protected]/data as
the managed location
Which SQL statement should you execute?

NO.31 You have an Azure Databricks workspace and a remote Git repository named Repo1. Repo1 contains two branches named main and Branch1.
You are on a development team that works in Repo1.
You commit changes to Branch1 and must merge the changes into main.
Before completing the merge, you need to meet the following requirements:
* Ensure that Branch1 includes the changes committed to main since Branch1 was created.
* Ensure that merge conflicts are detected and resolved.
What should you do first?

NO.32 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.dropna(subset=[“order_amount”])
Does this meet the goal?

NO.33 You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! contains three tasks named Task1, Task2. and Task3.
If Task1 fails, Task2 and Task3 must be prevented from running. Successfully completed tasks must NOT rerun during recovery.
You need to configure Job1 to support controlled failure handling and recovery What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

NO.34 You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline in Azure Databricks. The pipeline ingests transaction data into a table named Table1.
You need to ensure that in the event of an invalid record, the pipeline continues to run. The solution must meet the following requirements:
– Invalid records must NOT be written to Table1.
– Invalid records must be preserved for review.
– Minimize development effort.
What should you do?

NO.35 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.

NO.36 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.

NO.37 Case Study 1 – Contoso, Inc.
Overview
Company Information
Contoso, Inc. is a renewable energy provider that operates solar and wind farms across North America.
Existing Environment
Azure Environment
Contoso has a single Azure Databricks workspace named Workspace1 in the West US Azure region. Workspace1 is enabled for Unity Catalog.
Workspace1 contains all-purpose clusters for both development and production workloads.
The company’s Azure environment contains:
– In the West US, Central US, and East US Azure regions, Azure event hubs that stream telemetry data and an Azure Data Lake Storage Gen2 account in each region for each hub
– A single Azure SQL database in the West US region that hosts enterprise resource planning (ERP) data
– An Azure Database for PostgreSQL server in the West US region that stores operational maintenance data Data Environment Contoso ingests the following operational and business data:
– Telemetry data: More than 40,000 IoT sensors across 28 sites emit JSON telemetry events every few seconds. Each site sends the events to the nearest event hub, which writes the data into the corresponding Data Lake Storage Gen2 account. These files frequently experience schema drift.
– Maintenance logs: Maintenance systems generate historical repair logs, daily incremental updates, technician notes, and unstructured attachments that are stored in the Data Lake Storage Gen2 accounts.
– Operational maintenance data: Structured operational maintenance data is stored on the Azure Database for PostgreSQL server.
– External weather data: Hourly weather forecasts are retrieved from a REST API and written to the Data Lake Storage Gen2 accounts.
– ERP data: Daily CSV extracts of 50 to 100 GB contain equipment metadata, work orders, and purchase order information.
Problem Statements
The company’s existing analytics environment has several issues:
Ingestion
– Telemetry pipelines fall behind during peak loads.
– Telemetry ingestion fails when schema drift occurs.
– Streaming pipelines reprocess events after a pipeline restarts.
Compute
Production and development workloads run on the same all-purpose clusters.
Production and development workloads do NOT support autoscaling or workload isolation.
Governance
– The ERP data is duplicated across systems and development teams.
– Naming conventions are inconsistent across development teams, regions, and products.
– Ownership of the IoT sensors changes over time, and analysts must track the full history of the ownership.
– Occasionally, equipment manufacturers must correct data-entry mistakes in equipment names.
Historical values are NOT required.
Pipeline operations
– Pipelines lack resiliency, alerting, and centralized scheduling.
Requirements
Planned Changes
Contoso plans to implement the following changes:
– Implement scalable data pipeline orchestration.
– Create a managed analytics catalog in Unity Catalog.
– Implement a consistent approach to creating curated datasets.
– Establish a centralized governance model across ingestion, cleansed, and curated layers.
– Grant data engineers access to the ERP tables by using minimal development effort.
– Adopt a compute strategy that isolates production workloads and supports autoscaling.
– Adopt a slowly changing dimension (SCD) approach to address current data modeling issues.
Technical Requirements
Contoso identifies the following environment and compute requirements:
– Ensure that production ingestion workloads run on compute clusters that can scale automatically during telemetry spikes.
– Provide fast and consistent performance for business intelligence (BI) workloads.
– Prevent development activity from affecting production pipelines.
– Production ingestion workloads must run as scheduled, non-interactive pipelines rather than on shared interactive development clusters.
Contoso identifies the following data ingestion and processing requirements:
– Auto-scale ingestion pipelines to handle bursty workloads.
– Handle schema drift for the maintenance and telemetry data.
– Ingest file-based telemetry data by using minimal operational effort.
– Store all the ingested data in a format that supports incremental processing.
– Support the continuous ingestion of telemetry data from the event hubs by using exactly-once semantics.
– Support the ingestion of the structured maintenance data from the Azure Database for PostgreSQL server.
– Build a new telemetry pipeline that ingests raw events from the event hubs, cleanses the data, and publishes curated tables to Unity Catalog.
– Ensure that the Apache Spark Structured Streaming pipelines reading from the event hubs write the data into a managed Delta table named telemetry.raw_events. The pipelines must support schema drift and resume processing after failures without reprocessing the data.
Contoso identifies the following data modeling and optimization requirements:
– Build curated tables that standardize business logic.
– Overwrite equipment metadata attributes, such as name, manufacturer, model, and commissioning date, when the attributes change. Historical values are NOT required.
Contoso identifies the following pipeline deployment and operation requirements:
– Orchestrate multi-step ingestion and transformation workflows.
– Define a clear execution order and dependencies.
– Automatically retry failed steps and notify operators.
– Schedule ingestion and transformation workloads consistently.
Governance Requirements
Contoso identifies the following governance requirements:
– Centralize the metadata catalog.
– Provide isolated development areas that follow standard naming conventions.
– Establish a consistent structure for organizing raw, cleansed, and curated data.
– Provide a read-only mechanism to reference the ERP data through a foreign catalog.
Business Requirements
Contoso identifies the following business requirements:
– Improve ingestion reliability and reduce operational effort.
– Standardize data definitions across development teams.
Drag and Drop Question
Which SCD type should you use to support the planned data modeling changes? To answer, drag the appropriate types to the correct issues. Each type 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.