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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. 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.

2. Which tool is best for continuous ingestion of files landing in Azure Data Lake?

A) Auto Loader
B) Azure Logic Apps
C) Databricks Jobs
D) Azure Data Factory Copy Activity

3. Hotspot Question
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a data lifecycle and expiration solution that meets the following requirements:
- Transaction logs and deleted data files that are older than 90 days
must be removed from Delta tables to reclaim storage.
- All the tables must remain available for querying during the cleanup
process.
- Administrative effort must be minimized.
What should you do for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

4. Which feature provides end-to-end tracking of data movement in Databricks?

A) Cluster metrics
B) Query history only
C) Spark logs
D) Unity Catalog lineage

5. You have an Azure Databricks workspace that contains a Delta table named Table1.
Table1 has accumulated obsolete files.
You need to reduce storage costs. The solution must preserve 30 days of time travel history.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) Run the vacuum command on Table1.
B) Set the delta.deletedFileRetentionDuration table property to 30 days.
C) Reduce the deleted file retention period to one day.
D) Run the OPTIMIZE command on Table1.
E) Set the delta.logRetentionDuration table property to 30 days.

Solutions: