A manufacturing company wants to collect IoT sensor data from thousands of devices into Azure for real-time monitoring and predictive maintenance. The data must be ingested continuously, and the team needs to ensure that the pipeline can handle varying data volumes without manual scaling. What should the team implement?

Deploy a custom Python service on Azure Container Instances that polls the devices and writes to Azure SQL Database.

Create a scheduled Azure Data Factory pipeline that runs every hour to pull data from the devices via REST API and store it in Azure Blob Storage.

Set up an Azure IoT Hub and route messages to an Azure Stream Analytics job that writes to a hot path for real-time alerts and a cold path to Azure Data Lake Storage.

Use Azure Event Hubs to ingest the data and a separate Azure Function to process each event and store it in Azure Cosmos DB.

A telecom company needs to extract call detail records (CDRs) from a legacy mainframe system for AI-based network optimization. The mainframe only supports file-based export via FTP. The team needs to automate the extraction and ensure that the data is complete and accurate. What should they do?

Manually download the files from FTP and upload them to Azure Blob Storage.

Use Azure Data Factory with an FTP connector to pull the files on a schedule, and add a validation step that checks the record count in each file against a control file.

Use a Logic App with an FTP trigger to copy files to Azure Blob Storage.

Use AzCopy to copy the files from the FTP server to Azure Blob Storage on a schedule.

A retail analytics team needs to extract daily sales transaction data from a legacy on-premises SQL Server database into an Azure Data Lake for model training. The database is only accessible from within the corporate network, and the team has been granted read-only credentials. The extraction must run without manual intervention and must not impact production query performance. What should the team do?

Export the data manually to a CSV file each day and upload it to the Data Lake using Azure Storage Explorer.

Use Azure Data Factory with a managed virtual network and a private endpoint to connect directly to the on-premises SQL Server over the internet.

Create a logic app that runs on-premises using the on-premises data gateway and copies data directly to the Data Lake on a schedule.

Deploy an Azure Data Factory self-hosted integration runtime on a VM in the corporate network, create a linked service to SQL Server, and schedule a copy activity to the Data Lake.

PMICertified Professional in Managing AI (PMI-CPMAI)

Domain 3Objective 5

Gather Required Data PMI-CPMAI Practice Questions (Page 5)

Part of the Identify Data Needs domain, which accounts for 26% of the PMI-CPMAI exam. PMI does not publish an official question count, but from its 160-minute exam (~65–105 total, ~17–27 in this domain), expect 2–3 from this objective — we provide 24 practice questions to prepare you well beyond it. (estimate)

24questions here

5free pages

5concepts

26%of the exam

Questions 21–24

21application · medium
A manufacturing company wants to collect IoT sensor data from thousands of devices into Azure for real-time monitoring and predictive maintenance. The data must be ingested continuously, and the team needs to ensure that the pipeline can handle varying data volumes without manual scaling. What should the team implement?
Select an answer first
22application · medium
A telecom company needs to extract call detail records (CDRs) from a legacy mainframe system for AI-based network optimization. The mainframe only supports file-based export via FTP. The team needs to automate the extraction and ensure that the data is complete and accurate. What should they do?
Select an answer first
23application · medium
A retail analytics team needs to extract daily sales transaction data from a legacy on-premises SQL Server database into an Azure Data Lake for model training. The database is only accessible from within the corporate network, and the team has been granted read-only credentials. The extraction must run without manual intervention and must not impact production query performance. What should the team do?
Select an answer first
24foundation · easy
Which metric is commonly used to measure the completeness of a dataset?
Select an answer first

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Gather Required Data PMI-CPMAI Practice Questions (Page 5)

Questions 21–24

A telecom company needs to extract call detail records (CDRs) from a legacy mainframe system for AI-based network optimization. The mainframe only supports file-based export via FTP. The team needs to automate the extraction and ensure that the data is complete and accurate. What should they do?

Which metric is commonly used to measure the completeness of a dataset?

Keep studying — Identify Data Needs