Microsoft Updated AI-102 Exam Questions and Answers by alfie-james

Scenario: Support autocompletion and autosuggestion based on all product name variants.

A: Call a suggester-enabled query, in the form of a Suggestion request or Autocomplete request, using an API. API usage is illustrated in the following call to the Autocomplete REST API.

POST /indexes/myxboxgames/docs/autocomplete?search & api-version=2020-06-30

{

" search " : " minecraf " ,

" suggesterName " : " sg "

}

B: In Azure Cognitive Search, typeahead or " search-as-you-type " is enabled through a suggester. A suggester provides a list of fields that undergo additional tokenization, generating prefix sequences to support matches on partial terms. For example, a suggester that includes a City field with a value for " Seattle " will have prefix combinations of " sea " , " seat " , " seatt " , and " seattl " to support typeahead.

F. Use the default standard Lucene analyzer ( " analyzer " : null) or a language analyzer (for example, " analyzer " : " en.Microsoft " ) on the field.

Comprehensive Detailed Explanation

The requirement is to build an Adaptive Card for the chatbot that supports multilingual product display and meets accessibility requirements.

Product Name Display

The product JSON stores localized names like " name " : { " en " : " ... " , " es " : " ... " , " pt " : " ... " } .

To dynamically render based on the current language, you must use name[language].

This ensures the product name shown in the card adapts automatically to the user’s preferred language.

Stock Level Warning

Business rules require warnings when stock is low or out of stock.

This means you display the stock warning only when stockLevel is not ' OK ' .

Therefore, the correct condition is:

" $when " : " ${stockLevel != ' OK ' } "

Image Alt Text for Accessibility

Accessibility requirements specify that all images must have alt text in English, Spanish, and Portuguese.

In the product JSON, " alttext " is also localized:

" altText " : { " en " : " Bicycle " , " es " : " Bicicleta " , " pt " : " Bicicleta " }

To render the correct localized alt text dynamically, use image.altText[language].

Correct Selections:

First blank: name[language]

Second blank: " $when " : " ${stockLevel != ' OK ' } "

Third blank: image.altText[language]

Microsoft References

Adaptive Cards Templating

Adaptive Card $when property

Multilingual data in Azure Cognitive Search and Adaptive Cards

Project type: Object detection

Domain: General (compact)

The scenario requires building a Custom Vision model for a factory with intermittent internet connectivity. The solution must:

Detect defects in packaging

Locate the defects (provide bounding box information)

Ensure each package contains four products

Support offline use due to intermittent connectivity

Step 1 – Determine Project Type

Image Classification: Assigns a label to an image, but cannot locate where objects/defects are in the image.

Object Detection: Identifies what objects are present and their location (via bounding boxes).

Since the requirement is to identify location of defects and count products, the correct choice is Object detection.

Step 2 – Determine Domain

General domain: Standard general-purpose domain, optimized for cloud inference.

General (compact) domain: A lightweight version designed for exporting models to run offline on edge devices (iOS, Android, ONNX, etc.). Perfect for scenarios with intermittent internet connectivity.

Food domain: Specialized for food image classification, not defect detection in packaging.

Logo domain: Specialized for logo recognition, irrelevant here.

Since the requirement includes offline capability, the correct domain is General (compact).

Correct Answer:

Project type: Object detection

Domain: General (compact)

Microsoft References

Custom Vision project types: Classification vs Object detection

Domains in Custom Vision (General, General compact, Food, Logo, etc.)

Exporting compact models for offline use

You need to identify files containing specific phrases using cosine similarity .

Cosine similarity is used in vector embeddings , not in generative LLM completion.

text-embedding-ada-002 → Correct model for embeddings and cosine similarity search.

GPT-4, GPT-3.5 Turbo, GPT-4-32k → Generative models, not designed for embedding similarity.

Correct Answer: A. text-embedding-ada-002