AI-900 Practice Test 3 — 10 Questions

Question 1

A legal firm needs to automatically produce a short summary of each lengthy court ruling, highlighting the most important sentences. Which Azure AI Language feature should they use?

Accepted Answer

Extractive summarization. Extractive summarization (Option C) is the correct Azure AI Language feature because it identifies and extracts the most important sentences from a document to produce a concise summary. This directly matches the legal firm's requirement to automatically generate a short summary of lengthy court rulings by highlighting key sentences, without generating new text.

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Key phrase extraction

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Named entity recognition

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Sentiment analysis

Question 2

What is 'token pricing' in Azure OpenAI and what counts as a token?

Accepted Answer

A billing unit roughly equal to ¾ of an English word, counting both input and output. Azure OpenAI uses token-based pricing, where a token is a billing unit that represents roughly 0.75 of an English word. Both input (prompt) and output (completion) text are counted toward the total token usage, and the cost is calculated based on the total number of tokens consumed per API call.

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A billing unit roughly equal to one character in the input or output text

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A subscription-based pricing model where a fixed number of API calls are included monthly

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Authentication tokens required to secure API calls to Azure OpenAI

Question 3

A museum wants to create an interactive exhibit where visitors can type a description of a fictional creature, such as 'a fire-breathing dragon with emerald scales and golden wings,' and the system generates an image of that creature in real time. The museum must ensure that the generated images are safe and appropriate for all ages, including children. Which Azure service should they use, and which safety feature should they configure?

Accepted Answer

Azure OpenAI Service with the DALL-E 2 model and content filtering enabled. Azure OpenAI Service with DALL-E 2 is specifically designed for generating images from text descriptions, and enabling content filtering ensures the output is safe for all ages, including children. This combination directly meets the museum's requirement for real-time, safe image generation from textual prompts.

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Azure Cognitive Services Computer Vision with custom vision image generation

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Azure OpenAI Service with the GPT-4 model and content filtering enabled

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Azure OpenAI Service with the DALL-E 2 model without content filtering

Question 4

A developer uses Azure OpenAI Service to generate product descriptions. Each description must be concise and not exceed 50 words. Which parameter should the developer set in the API request to control the output length?

Accepted Answer

max_tokens. The `max_tokens` parameter in the Azure OpenAI API directly controls the maximum number of tokens (words or subwords) in the generated output. By setting `max_tokens` to a value that corresponds to 50 words, the developer ensures the model stops generating once the limit is reached, producing concise descriptions.

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Temperature

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top_p

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frequency_penalty

Question 5

What is named entity recognition (NER) and provide an example of its output?

Accepted Answer

NER identifies and categorizes named entities like people, organizations, locations, and dates in text. Named entity recognition (NER) is a natural language processing (NLP) capability that identifies and classifies key elements in text into predefined categories such as person names, organizations, locations, dates, and quantities. Option B correctly describes this function, and its output typically includes the extracted entity along with its category label, for example, {'entity': 'Microsoft', 'category': 'Organization'}.

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NER identifies grammatical parts of speech like nouns and verbs

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NER generates new names for products based on brand guidelines

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NER converts names into anonymous placeholders for privacy

Question 6

What is 'model interpretability' and which Azure tool helps with it?

Accepted Answer

Understanding why a model makes specific predictions by identifying influential features — supported by Azure ML's Responsible AI dashboard. Model interpretability refers to the ability to understand and explain why a machine learning model makes specific predictions, typically by identifying which input features most influenced the output. Azure Machine Learning's Responsible AI dashboard directly supports this through built-in interpretability components like feature importance plots and error analysis, enabling developers to debug models and build trust. Option B correctly pairs the definition with the specific Azure tool that implements it.

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Understanding what programming language a model was written in

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Translating model documentation into multiple languages

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Monitoring how quickly a model responds to prediction requests

Question 7

What is the Azure AI Vision background removal feature used for?

Accepted Answer

Automatically separating foreground subjects from the background in images. Azure AI Vision background removal is designed to automatically separate foreground subjects from the background in images, producing a mask or a cut-out of the primary object. This feature uses deep learning models to identify and isolate the main subject, enabling further processing like compositing or analysis without the background.

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Blurring the background to create depth of field effects

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Identifying what type of background (indoor/outdoor) is in an image

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Replacing backgrounds in video calls

Question 8

What is 'sentiment analysis' at the opinion mining level vs. document level in Azure AI Language?

Accepted Answer

Document-level assigns one overall sentiment; opinion mining extracts per-aspect sentiments. In Azure AI Language, document-level sentiment analysis assigns a single overall sentiment (positive, negative, neutral, or mixed) to the entire document, while opinion mining (a feature of aspect-based sentiment analysis) extracts sentiments for specific aspects or targets within the text, such as 'service' or 'food' in a restaurant review. This allows for granular, per-aspect sentiment detection rather than a single aggregate score.

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Document-level is more accurate; opinion mining is a faster but less precise approximation

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Opinion mining works only on social media posts; document-level works on all text types

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Document-level sentiment requires training data; opinion mining is pre-built

Question 9

What is 'Azure AI Vision's image vectorisation' and how does it enable image search?

Accepted Answer

Converting images to semantic embedding vectors for similarity-based search and retrieval. Azure AI Vision's image vectorisation converts images into semantic embedding vectors—numerical representations that capture the visual content and meaning of an image. These vectors enable similarity-based search by allowing the system to compare the vector of a query image against a database of pre-computed image vectors, returning the most visually or semantically similar results.

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Converting image files to a vectorised (lossless) format like SVG for web use

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Drawing vector graphics from a description of an image's contents

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Optimising image file size by converting to the most efficient vector format

Question 10

A data scientist trains a regression model to predict energy consumption for a smart building. The model achieves very low error on the training data but performs significantly worse on a held-out validation set. Which technique would most directly address this problem?

Accepted Answer

Regularization. The model's low training error but high validation error indicates overfitting, where the model has memorized the training data rather than learning generalizable patterns. Regularization (e.g., L1 or L2) directly penalizes large coefficients, reducing model complexity and improving generalization to unseen data.

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Feature engineering

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Cross-validation

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Hyperparameter tuning