AI-900 Practice Test 49 — 15 Questions

Question 1

A software company uses Azure OpenAI to generate code snippets. They want to evaluate how confident the model is in each token it generates. Which Azure OpenAI feature provides a numerical measure of confidence for each generated token?

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

Accepted Answer

Logprobs. Logprobs (log probabilities) is the Azure OpenAI feature that provides a numerical measure of confidence for each generated token. It outputs the log probability of each token being selected by the model, allowing developers to assess how certain the model is about its predictions at the token level.

Answer

Temperature

Answer

Top-p

Answer

Presence penalty

Question 2

A company wants to use Azure OpenAI Service to generate product descriptions. They need to ensure the model's output is based on their specific product catalog and pricing, not on generic information. Which approach should they use?

Accepted Answer

Implementing Retrieval Augmented Generation (RAG) with their catalog.. Retrieval Augmented Generation (RAG) allows the model to dynamically retrieve relevant product catalog and pricing information from an external knowledge base at inference time, ensuring the generated descriptions are grounded in the company's specific data rather than relying on the model's generic training data. This approach avoids the need for costly fine-tuning and keeps the output up-to-date without retraining.

Answer

Fine-tuning the model on their product catalog.

Answer

Using few-shot learning with examples.

Answer

Increasing the temperature parameter.

Question 3

A data scientist trains a model to predict house prices using features like number of bedrooms, square footage, and location. The model achieves a mean absolute error (MAE) of $5,000 on the training data but $25,000 on the test data. Which problem is the model most likely experiencing?

Accepted Answer

Overfitting. The model performs well on training data (MAE $5,000) but poorly on test data (MAE $25,000), which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training data too well, failing to generalize to unseen data. In Azure Machine Learning, this can be detected by comparing training vs. validation metrics and is often mitigated using regularization techniques or simplifying the model.

Answer

Underfitting

Answer

Multicollinearity

Answer

Class imbalance

Question 4

A security company needs to monitor a warehouse using video cameras. They want to detect whether any persons are present in a given frame and also know their approximate locations. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct choice because it not only identifies whether persons are present in a video frame but also provides bounding box coordinates indicating their approximate locations. This capability is specifically designed to locate multiple objects of interest within an image, which directly matches the requirement of detecting persons and knowing where they are.

Answer

Image classification

Answer

Semantic segmentation

Answer

Optical Character Recognition (OCR)

Question 5

A customer support team wants to automatically extract the most important words or short phrases from each customer service ticket to understand common issues. Which Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct Azure AI Language feature because it is specifically designed to identify and return the most important words and short phrases from a document, such as a customer service ticket. This allows the support team to automatically surface common issues by analyzing the extracted key phrases across many tickets. The other options serve different purposes: named entity recognition identifies specific entities like people or organizations, sentiment analysis detects emotional tone, and language detection identifies the language of the text.

Answer

Named entity recognition

Answer

Sentiment analysis

Answer

Language detection

Question 6

A data scientist trains a model to predict the exact number of cars that will cross a bridge each day for maintenance planning. The model uses historical traffic data as input. Which type of machine learning task is this?

Accepted Answer

Regression. The model predicts a continuous numerical value (the exact number of cars) based on historical traffic data. Regression is the correct machine learning task for predicting continuous numeric outcomes, such as counts, prices, or temperatures, making option B correct.

Answer

Classification

Answer

Clustering

Answer

Reinforcement learning

Question 7

A consumer electronics company collects online reviews about their latest smartphone. They want to identify specific aspects that customers praise or criticize, such as battery life, camera quality, and screen brightness. Which Azure AI Language feature should they use to extract these aspect-based opinions?

Accepted Answer

Sentiment analysis with opinion mining. Sentiment analysis with opinion mining is specifically designed to extract aspect-based opinions from text. In this scenario, the company needs to identify which aspects (e.g., battery life, camera quality) are praised or criticized, which requires both aspect detection and sentiment polarity assignment. Opinion mining extends standard sentiment analysis by linking sentiments to specific targets or aspects within the text.

Answer

Key phrase extraction

Answer

Named entity recognition

Answer

Language detection

Question 8

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?

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 9

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 10

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 11

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 12

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 13

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 14

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 15

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)