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

Customer-managed key (CMK) encryption does not affect throttling.

Instead, you could migrate to a Cognitive Search service that uses a higher tier.

Note: A simple fix to most throttling issues is to throw more resources at the search service (typically replicas for query-based throttling, or partitions for indexing-based throttling). However, increasing replicas or partitions adds cost, which is why it is important to know the reason why throttling is occurring at all.

Azure AI Content Understanding supports common document and image formats including .pdf, .docx, .jpg, and .png. .webp isn’t listed among supported types. Therefore, you can analyze File1.pdf, File2.jpg, File3.docx, and File5.png, but not File4.webp. Microsoft Learn

Microsoft References:

Content Understanding: service limits and supported file types. Microsoft Learn

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 The subscription will be charged 86 tokens for the execution of the session. No

 The text completion was truncated because the Max response tokens value was exceeded. No

 The prompt_tokens value will be included in the calculation of the Max response tokens value. No

From the response payload:

usage.prompt_tokens = 37

usage.completion_tokens = 86

usage.total_tokens = 123

finish_reason = "stop"

Billing is for total tokens (input + output), not just completion tokens.Azure OpenAI bills on both prompt (input) and completion (output) tokens. Here, the total is 123 tokens, so it is not just 86. Hence No.

Not truncated by max response tokens.If a response hits the maximum allowed output tokens, finish_reason would be "length". Here it is "stop" and the model produced only 86 completion tokens while Max response tokens was set to 100, so the limit was not reached. Hence No.

max response tokens applies only to the generated output, not the prompt.max_tokens (Max response tokens) limits the number of tokens the model may generate in the completion; it does not include prompt_tokens. Hence No.

Microsoft References

Azure OpenAI Service – Chat Completions API (parameters, max_tokens, response usage, finish_reason).https://learn.microsoft.com/azure/ai-services/openai/reference#chat-completions

Azure OpenAI pricing and token usage model (charges for input and output tokens).https://learn.microsoft.com/azure/ai-services/openai/overview#pricing

Token counting and limits (context vs. max output tokens).https://learn.microsoft.com/azure/ai-services/openai/concepts/tokenizers#token-limits

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.

Searchable → Field content is tokenized and added to the full-text index (supports free-text queries).

Retrievable → Field values can be included in search results.

Sortable → Used for ordering results, not required here.

Facetable → Used for aggregation/filtering by categories, not required here.

Filterable → Used for structured filters (e.g., WHERE conditions), not required here.

The task is to process incoming emails and route them to French or English support teams. This means we need to detect the language of each email.

Let’s break it down:

Which Cognitive Service is needed?

Azure Text Analytics (part of Azure AI Language) provides a Language Detection API.

The Translator service is used for translation, not just detecting the original language. Since the requirement is only to decide whether to send emails to French or English teams, we just need to detect the language.

Endpoint selection

Text Analytics uses regional endpoints such as:https:// .api.cognitive.microsoft.com/

For example: eastus.api.cognitive.microsoft.com is valid.

api.cognitivemicrosofttranslator.com → incorrect (this is for Translator service).

portal.azure.com → Azure portal, not an API endpoint.

API Path selection

To detect language, you call:/text/analytics/v3.1/languages

/text/analytics/v3.1/entities/recognition/general → entity recognition, not language.

/translator/text/v3.0/... → translation, not language detection.

Correct Answer Combination:

eastus.api.cognitive.microsoft.com

/text/analytics/v3.1/languages

Text Analytics Language Detection API

Text Analytics REST API v3.1

Microsoft References

Scenario Recap:

You have an Azure OpenAI resource with multiple deployed models.

You are using the Chat playground to build a chatbot.

Requirements:

Generate text in concise, formal business language.

Reduce cost of running the model.

Maintain the size of the chatbot history window (i.e., context length).

Step 1: Ensuring concise, formal business languageIn the Chat playground setup panel, you can configure System message instructions.

Example:

You are an AI assistant that responds in concise formal business language.

This controls tone, style, and response behavior.

So one setting is: System message / Instructions.

Step 2: Reducing cost while maintaining chat history windowCosts in Azure OpenAI are tied to:

The model used (larger models = more expensive).

The context window size (token usage).

Since we must maintain the size of the chat history window, we cannot shrink context length.

Therefore, to reduce cost: select a smaller/cheaper deployed model (e.g., GPT-3.5 instead of GPT-4).

So the second setting is: Deployment (model selection).

Configure Deployment → choose a smaller/cheaper model.

Configure System message instructions → specify “concise formal business language.”

✅ Final Answer:

Microsoft References:

Azure OpenAI Chat playground

Best practices for reducing token cost

Comprehensive Detailed ExplanationThe scenario is identical to Question 43:

You have a FindContact intent.

Training phrases include city names (London, Seattle, Ukraine).

You must model location properly in Language Understanding.

Why the Proposed Solution is WrongThe solution says: “Create a new entity for the domain.”

Entities in LUIS/CLU represent specific data types to extract (e.g., location, date, contact name).

Domains in Language Understanding are prebuilt sets of intents and entities for common use cases (like Calendar, Home Automation, Communication).

Creating an “entity for the domain” is conceptually incorrect — entities are not meant to represent domains.

What we really need is to model location as an entity (for example, using the prebuilt GeographyV2 entity), not to create a generic “domain entity.”

Keep the FindContact intent.

Add a Location entity (prebuilt GeographyV2 or custom “Location”).

Train the model so that London, Seattle, Ukraine are recognized as entity values within the FindContact intent.

Correct Approach

A. Yes → Incorrect. This would not meet the goal.

B. No → Correct. Creating a domain entity is not the right way; instead, a proper location entity should be added.

Option Analysis

Correct Answer: B. No

Intents vs Entities in Language Understanding

Prebuilt entity: GeographyV2

LUIS domains overview

Microsoft References

You have files with the following formats:

File1: WMV (34 mins, 400 MB)

File2: AVI (90 mins, 1,200 MB)

File3: MOV (300 mins, 980 MB)

File4: MP4 (80 mins, 1,800 MB)

You must determine which files can be uploaded to Azure AI Video Indexer.

Supported formats: MP4, MOV, WMV, AVI, M2TS.

Maximum file length: 4 hours (240 minutes).

Maximum file size: 2 GB (2048 MB) for website uploads.

Key Rules (Video Indexer upload limitations):

File1 (WMV, 34 min, 400 MB): Supported format, within time and size → ✅ Allowed.

File2 (AVI, 90 min, 1,200 MB): Supported format, within time and size → ✅ Allowed.

File3 (MOV, 300 min, 980 MB): Supported format, but exceeds 240 min limit → ❌ Not Allowed.

File4 (MP4, 80 min, 1,800 MB): Supported format, within size (under 2 GB), within time → ✅ Allowed.

Check each file:

Correct Answer:

A. File1, File2, and File4 only

Microsoft Reference:

Video Indexer upload limitations