AI-900 Practice Test 49 — 15 Questions

Question 1

A retail company develops an AI system that recommends products to customers based on their purchase history. They want to ensure that the recommendations are not biased against any demographic group. Which Microsoft responsible AI principle is most directly relevant?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Fairness. The Fairness principle is most directly relevant because it requires AI systems to treat all demographic groups equitably, avoiding biases in outcomes such as product recommendations. In this scenario, the company must ensure that the recommendation model does not systematically favor or disadvantage any group based on protected attributes like age, gender, or ethnicity, which is a core concern of fairness in AI.

Answer

Inclusiveness

Answer

Reliability and safety

Answer

Transparency

Question 2

A quality control manager at a bottling plant needs an automated system to inspect images of bottles coming off the production line. The system must determine whether each bottle has a correctly sealed cap or is defective (cap missing or crooked). The manager has a set of labeled images showing both acceptable and defective bottles. Which Azure Computer Vision service should they use to build a model that classifies each bottle image as 'acceptable' or 'defective'?

Accepted Answer

Azure Custom Vision (Image Classification). Azure Custom Vision (Image Classification) is the correct service because it allows you to upload labeled images of bottles (acceptable and defective) and train a custom image classification model to distinguish between the two classes. This service is specifically designed for scenarios where you need to classify images into user-defined categories without requiring deep learning expertise.

Answer

Azure Face API

Answer

Azure Form Recognizer

Answer

Azure OCR (Read API)

Question 3

An autonomous vehicle team needs a system that not only identifies objects like cars and pedestrians but also creates a precise pixel-level mask for each individual object instance, even when objects overlap. Which Azure Computer Vision capability should they use?

Accepted Answer

Instance segmentation. Instance segmentation (Option D) is the correct choice because it combines object detection with semantic segmentation to identify each individual object instance and generate a precise pixel-level mask for it, even when objects overlap. This capability is essential for autonomous vehicles to distinguish between multiple cars or pedestrians that may partially occlude each other, enabling safe navigation.

Answer

Image classification

Answer

Object detection

Answer

Semantic segmentation

Question 4

What is 'temperature' parameter in Azure OpenAI and how does it affect output?

Accepted Answer

A parameter controlling output randomness — low values are deterministic, high values are creative. The temperature parameter in Azure OpenAI controls the randomness of the model's output. A low temperature (e.g., 0.0) makes the model deterministic, always choosing the most likely next token, while a high temperature (e.g., 1.0 or above) increases randomness, allowing for more creative and varied responses. This parameter directly influences the probability distribution over tokens before sampling.

Answer

The compute temperature of GPU hardware during inference, affecting speed

Answer

The time limit before a model inference request times out

Answer

The sensitivity of the model's content filter — higher blocks more content

Question 5

What is the 'inclusiveness' principle in Microsoft's responsible AI framework?

Accepted Answer

AI systems should be designed to benefit and empower all people, including marginalized groups. The 'inclusiveness' principle in Microsoft's responsible AI framework mandates that AI systems should be designed to benefit and empower all people, including marginalized groups. This ensures that AI solutions do not perpetuate bias or exclude underrepresented populations, aligning with Microsoft's commitment to fairness and accessibility in AI workloads.

Answer

AI systems should be available in all countries without restriction

Answer

AI systems should be open-source and freely available

Answer

AI systems should include all possible features regardless of relevance

Question 6

A data scientist is building a model to predict the exact temperature in degrees Celsius based on humidity and atmospheric pressure. The model will output a single numeric value for each input. Which type of machine learning task is this?

Accepted Answer

Regression. This is a regression task because the goal is to predict a continuous numeric value (temperature in degrees Celsius) from input features (humidity and atmospheric pressure). Regression models output a real number, unlike classification which predicts discrete categories. In Azure Machine Learning, regression algorithms like Linear Regression or Decision Forest Regression are used for such tasks.

Answer

Classification

Answer

Clustering

Answer

Object detection

Question 7

A customer service company uses Azure OpenAI Service to generate automated replies to customer inquiries. They want each reply to adopt a polite and empathetic tone. Which configuration should they use to guide the model's behavior without retraining?

Accepted Answer

Define a system message that instructs the model to be polite and empathetic.. A system message in Azure OpenAI Service allows you to set the context and tone for the model's responses without retraining. By defining a system message that instructs the model to be polite and empathetic, you guide the model's behavior at inference time, ensuring replies adopt the desired tone.

Answer

Set the temperature parameter to a high value (e.g., 1.0).

Answer

Set the top_p parameter to a low value (e.g., 0.1).

Answer

Set the max_tokens parameter to a specific value (e.g., 150).

Question 8

A customer support team receives thousands of unstructured chat transcripts every day. They want to automatically identify the most common recurring issues (e.g., 'long wait time', 'payment error', 'login problem') without training a custom model. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it automatically identifies the most salient topics or 'key phrases' from unstructured text without requiring any custom training. In this scenario, the recurring issues like 'long wait time' or 'payment error' are exactly the type of multi-word, topic-level phrases that key phrase extraction surfaces, making it the ideal prebuilt feature for summarizing common support topics.

Answer

Named entity recognition (NER)

Answer

Sentiment analysis

Answer

Language detection

Question 9

Which Azure AI service extracts key information (like invoice numbers, dates, and amounts) from structured documents like forms and invoices?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract structured data such as invoice numbers, dates, and amounts from forms and invoices. It uses prebuilt models for invoices and receipts, along with custom model training, to perform optical character recognition (OCR) and key-value pair extraction from structured documents.

Answer

Azure AI Language

Answer

Azure AI Vision

Answer

Azure AI Translator

Question 10

An autonomous drone needs to navigate a forest by identifying individual trees, including their exact shape and boundaries, to avoid colliding with branches. The drone also needs to distinguish between trees and other objects like rocks. Which Azure Computer Vision capability is best suited for this requirement?

Accepted Answer

Semantic segmentation. Semantic segmentation is the correct choice because it classifies every pixel in an image, assigning each pixel to a specific class (e.g., 'tree', 'rock', 'branch'). This pixel-level precision allows the drone to identify the exact shape and boundaries of individual trees, which is essential for collision avoidance in a forest environment.

Answer

Image classification

Answer

Object detection

Answer

Optical character recognition (OCR)

Question 11

What is 'stochastic gradient descent' (SGD) and how does it work?

Accepted Answer

An optimisation algorithm that updates weights using gradients computed on random data mini-batches. Stochastic Gradient Descent (SGD) is an optimization algorithm used to train machine learning models by iteratively updating model weights. It computes the gradient of the loss function on a randomly selected mini-batch of training data (not the entire dataset), which introduces noise but significantly speeds up convergence and reduces memory usage. This mini-batch approach is the core of SGD and distinguishes it from batch gradient descent.

Answer

A random sampling method for selecting training data without replacement

Answer

A technique for randomly selecting which model architecture to use for AutoML

Answer

Randomly descending through decision tree branches to make predictions

Question 12

A marketing team wants to create original images for advertisements based on text descriptions. Which Azure OpenAI Service model capability should they use?

Accepted Answer

DALL-E. DALL-E is the correct choice because it is the Azure OpenAI Service model specifically designed for generating original images from natural language text descriptions. Unlike other models in the suite, DALL-E uses a diffusion-based architecture to create photorealistic or stylized visuals based on prompt inputs, making it ideal for the marketing team's goal of producing custom advertisement imagery.

Answer

GPT-3.5

Answer

Codex

Answer

Azure Speech-to-Text

Question 13

What is 'online learning' (incremental learning) in machine learning?

Accepted Answer

Continuously updating model weights on new data as it arrives rather than batch retraining. Online learning (incremental learning) is a machine learning technique where the model is updated continuously as new data arrives, rather than retraining from scratch on the entire dataset. This is essential for scenarios with streaming data or when retraining on all historical data is computationally prohibitive. In Azure, this is supported by services like Azure Stream Analytics and Azure Machine Learning's online endpoints, which can update model weights incrementally.

Answer

Training ML models through an online learning management system

Answer

Requiring an internet connection during model training for cloud compute access

Answer

A training approach where users can interact with and correct the model in real time

Question 14

What is a copilot in the context of Microsoft AI products?

Accepted Answer

An AI assistant integrated into products that helps users complete tasks using natural language. A copilot in Microsoft AI products, such as Microsoft 365 Copilot or GitHub Copilot, is an AI assistant that uses large language models (LLMs) and natural language processing to help users complete tasks like drafting documents, generating code, or summarizing emails. It integrates directly into the user interface of applications (e.g., Word, Excel, Teams) and interprets natural language prompts to produce contextually relevant outputs, leveraging Azure OpenAI Service under the hood.

Answer

A hardware accelerator for AI model training

Answer

A type of database for storing conversation history

Answer

A software testing tool for AI models

Question 15

A financial analyst uses Azure OpenAI Service to generate summaries of quarterly earnings reports. The analyst provides the raw text of the report in the prompt and wants the summary to stick strictly to the facts presented in that text, without adding any external information or speculation. Which technique should the analyst employ to minimize the risk of the model inventing information?

Accepted Answer

Use grounding by including the report text in the prompt and explicitly instructing the model to base the summary only on that text.. Grounding the model with the source text and explicitly instructing it to base the summary solely on that text is the most direct way to reduce hallucination. Azure OpenAI Service relies on the prompt for context; by providing the raw report and a strict instruction, the model is constrained to extract facts from the provided content rather than generating novel information.

Answer

Set the temperature parameter to a high value.

Answer

Set the frequency penalty to the maximum allowed value.

Answer

Set the max_tokens parameter to a very small number.