Pass the Microsoft Azure AI Engineer Associate AI-102 Questions and answers with Dumpstech

A streaming workload means data is generated continuously and ingested in near real time.

Telemetry from edge/IoT devices is a classic example of streaming workloads because the devices constantly push data (sensor readings, logs, metrics).

A. sending transactions daily → batch workload (not streaming).

B. sending metadata every 30 minutes → periodic batch, not continuous streaming.

C. sending old transactions to archive → cold data archival, not streaming.

Correct Answer: D

Step 1: Export the model using the Export for containers (GZIP) option.

Export versioned app ' s package from LUIS portal

The versioned app ' s package is available from the Versions list page.

Sign on to the LUIS portal.

Select the app in the list.

Select Manage in the app ' s navigation bar.

Select Versions in the left navigation bar.

Select the checkbox to the left of the version name in the list.

Select the Export item from the contextual toolbar above the list.

Select Export for container (GZIP).

The package is downloaded from the browser.

Step 2: Select v1.1 of app1.

A trained or published app packaged as a mounted input to the container with its associated App ID.

Step 3: Run a contain and mount the model file.

Run the container, with the required input mount and billing settings.

To mask (redact) phone numbers and email addresses in text using Azure AI Language, you should call the PII entity recognition operation and then print the redacted text that the service returns. In the SDKs, this corresponds to calling RecognizePiiEntities and reading the RedactedText from the result object. The PII feature automatically detects categories such as PhoneNumber and Email and returns a redacted version of the input (masked) in the redactedText/RedactedText field. This is exactly what the code snippet’s “Masked Text” output is asking for.

Therefore:

Use RecognizePiiEntities to detect and redact PII in the document.

Output RedactedText to print the masked string.

Microsoft References

How-to: Detect and redact PII in text (overview and redaction behavior). Microsoft Learn

Quickstart: Detect Personally Identifiable Information (shows PII detection and redacted output). Microsoft Learn

NET reference: PiiEntityCollection.RedactedText property (“Gets the text of the input document with all PII redacted”). Microsoft Learn

Python client overview: recognize_pii_entities operation for PII detection. Microsoft Learn

The system must analyze multiple sensor data points (rotation speed, angle, temperature, pressure) and generate alerts on anomalies.

Application Insights → used for monitoring applications, not IoT sensor data.

Metric alerts in Azure Monitor → works with single metrics but does not handle complex correlations.

Univariate Anomaly Detection → only works on a single time series (e.g., just temperature).

Multivariate Anomaly Detection → specifically built to detect anomalies across multiple correlated variables (sensor data).

Thus, the correct choice is Multivariate Anomaly Detection.

Answer Area

Open the Review suggestions pane.

This step allows you to access suggestions generated by the Azure AI Language service based on user interactions and queries. Reviewing these suggestions helps identify potential improvements or alternative phrasings that can enhance the model ' s understanding and accuracy with minimal effort.

Review and accept the alternative phrases.

After opening the review suggestions pane, you can evaluate and accept alternative phrases or questions that users might have asked. Accepting these updates trains the model to recognize variations in how questions are posed, thereby improving its ability to provide accurate responses without requiring extensive manual retraining.

Enable active learning.

Enabling active learning allows the model to continuously improve by learning from user interactions and feedback over time. This feature automatically suggests refinements based on real-world usage, minimizing development effort while enhancing the chatbot ' s accuracy as it adapts to common questions.

Rationale

These steps are sequenced to progressively improve the chatbot ' s performance with minimal development effort. Starting with reviewing suggestions leverages existing data, accepting alternative phrases refines the model ' s understanding, and enabling active learning ensures ongoing improvement, aligning with the goal of increasing accuracy efficiently.

A data analyst

The statement is:

“________ is responsible for creating visuals and charts that help a company make informed decisions.”

Let’s analyze the options:

A data analyst

Main responsibility: interpreting data, creating visuals, dashboards, and reports using tools like Power BI, Excel, or Tableau.

Helps stakeholders make business decisions from data.

Correct answer.

A data engineer

Focuses on designing, building, and maintaining data pipelines, ETL processes, and storage systems.

Not primarily responsible for creating visuals.

A data scientist

Specializes in advanced analytics, machine learning, and statistical modeling.

They may present insights but their focus is on predictions and algorithms, not routine visuals.

A database administrator (DBA)

Responsible for managing databases, ensuring security, availability, backups, and performance.

Not responsible for creating visuals or dashboards.

Correct Answer: A data analyst

Microsoft Reference

Roles in data analytics

Microsoft Learn: Data Analyst role description — responsible for creating reports and visualizations to support decision-making.

To let an agent use public web data with recency controls (e.g., limit to content from the last 90 days), you add the Bing grounding tool. In the Azure AI Foundry Agent Service SDK, you create a connection (e.g., search_connection_id ) and then construct a BingGroundingTool with that connection. The tool exposes a list of definitions (tool operations) that the agent can call at runtime.

When creating the agent, you pass tool definitions via the tools= argument (a list of ToolDefinition ). The tool_resources= argument is used when the tool requires additional resource descriptors (e.g., Azure AI Search index configuration). For Bing grounding, providing the connection and passing the definitions as tools= is sufficient. This enables the agent to call Bing with date filters to retrieve only recent, publicly accessible content.

Microsoft Azure AI References (titles only)

Azure AI Agent Service – Bing web grounding tool

Azure AI Agent Service – Create an agent (agent creation parameters including tools and tool_resources )

Azure AI Agent Service – Tool connections and definitions

You are using Azure Cognitive Search, where access is controlled by:

Admin keys: Provide full read/write control (never given to apps).

Query keys: Provide read-only access, meant for client apps.

Here, the query key has been compromised. You want to minimize downtime and ensure continued read-only access.

Step 1 – Add a new query key

First, generate a new query key from the Azure portal or via REST API.

This allows you to continue providing read-only access to authorized apps.

Step 2 – Change the app to use the new key

Update the application configuration so that it authenticates requests with the new query key.

This ensures uninterrupted service while preparing to retire the compromised key.

Step 3 – Delete the compromised key

Once the app is using the new key successfully, revoke the compromised query key.

This prevents further unauthorized usage.

Why not other options?

Regenerate primary/secondary admin key: Not necessary since only the query key is compromised, and admin keys give full control.

Change app to use secondary admin key: Insecure (admin keys should never be exposed to client apps).

Correct Sequence

Add a new query key.

Change the app to use the new key.

Delete the compromised key.

Microsoft References

Manage admin and query keys in Azure Cognitive Search

Create and use query keys

Scenario Recap:

IoT data from 100 machines × 50 sensors × 1-minute intervals for 1 year = 5,000 time series datasets .

Requirement: Detect unusual values (outliers) to predict failures.

Correct Service:

Azure Anomaly Detector (A) is designed for time-series anomaly detection.

It learns temporal patterns and detects anomalies in sensor/IoT data streams.

Other options:

Cognitive Search = full-text search

Form Recognizer = extract text/fields from documents

Custom Vision = image classification

Answer: A. Anomaly Detector