AI-900 Practice Test 16 — 15 Questions

Question 1

A hospital wants to create a system that can transcribe doctor-patient conversations in real time and also extract medical conditions, medications, and dosages from the transcribed text. Which combination of Azure AI services should they use?

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

Accepted Answer

Speech to Text and Text Analytics for Health. The scenario requires real-time transcription of doctor-patient conversations, which is handled by Azure Speech to Text, and then extraction of medical entities like conditions, medications, and dosages from the transcribed text, which is specifically provided by Azure Text Analytics for Health. Text Analytics for Health is a specialized container or API within Azure Cognitive Services that uses medical ontologies (e.g., UMLS, SNOMED CT) to extract clinical entities, unlike the standard Text Analytics API which only extracts general entities like names or locations.

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Speech to Text and Text Analytics API (standard)

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Translator Text and Language Understanding (LUIS)

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Speaker Recognition and Question Answering

Question 2

A customer insights team at a retail company has collected thousands of product reviews. They want to automatically extract the most frequently mentioned topics or aspects from these reviews, such as 'price', 'durability', and 'customer service', without any custom training. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct choice because it is a prebuilt Azure AI Language feature designed to automatically identify and extract the main points or topics from unstructured text, such as product reviews, without requiring any custom training or labeled data. It surfaces frequently mentioned aspects like 'price', 'durability', and 'customer service' by analyzing linguistic patterns and term frequency, making it ideal for this summarization task.

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Text Analytics for Health

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Conversational Language Understanding

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Entity Linking

Question 3

A developer wants to build a virtual assistant that can understand user intents such as 'Book a flight' or 'Check weather' and extract relevant entities like destination and date. The developer has a small set of labeled example utterances. Which Azure AI Language feature should the developer use?

Accepted Answer

Conversational Language Understanding (CLU). Conversational Language Understanding (CLU) is the correct Azure AI Language feature because it is specifically designed to extract both intents (e.g., 'Book a flight') and entities (e.g., destination, date) from user utterances. The developer has a small set of labeled examples, which CLU can use to train a custom model for intent recognition and entity extraction, making it ideal for building a virtual assistant.

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Custom Question Answering

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Custom Text Classification

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Named Entity Recognition (NER)

Question 4

A large company deploys an AI system to screen job applications and recommend candidates for interviews. After six months, an audit reveals that the system recommends candidates from certain ethnic groups at a much lower rate than others, even when those candidates have similar qualifications. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Fairness. The scenario describes an AI system that produces biased outcomes against certain ethnic groups despite similar qualifications, which directly violates the Fairness principle. Fairness in responsible AI requires that systems treat all people equitably and do not discriminate based on sensitive attributes like ethnicity, race, or gender. The audit finding shows the system is not fair, as it systematically disadvantages specific groups.

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Inclusiveness

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Reliability and safety

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Privacy and security

Question 5

What is 'token budget' and 'context window' management in large language models?

Accepted Answer

The maximum text the model can process in one call, requiring strategic management of what fits in context. 'token budget' and 'context window' refer to the maximum number of tokens (words, subwords, or characters) a large language model can process in a single inference call. The context window is a fixed limit (e.g., 4096 tokens for GPT-3.5, 8192 for GPT-4), and managing the token budget involves strategically truncating, summarizing, or prioritizing input text to fit within this window, ensuring the model can generate coherent and relevant responses without exceeding its capacity.

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The monthly spending limit set for an Azure OpenAI subscription

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The number of API calls allowed per minute before rate limiting kicks in

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A pre-purchase of tokens at a discounted rate for high-volume Azure OpenAI users

Question 6

What is the difference between a chatbot and a conversational AI agent?

Accepted Answer

Chatbots use fixed rules/decision trees; conversational AI uses NLP/ML for flexible, context-aware responses. Chatbots traditionally rely on predefined rules or decision trees to handle user inputs, limiting them to scripted interactions. In contrast, conversational AI agents leverage natural language processing (NLP) and machine learning (ML) to understand intent, manage context, and generate dynamic, human-like responses. This allows conversational AI to handle ambiguous phrasing, maintain multi-turn dialogue state, and adapt to user behavior without explicit programming for every scenario.

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Chatbots are always voice-based; conversational AI is text-only

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Chatbots are more expensive to build than conversational AI

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They are the same technology with different marketing terms

Question 7

A data scientist has a dataset containing thousands of labeled images of cats and dogs. The data scientist wants to train a model that can automatically classify new unlabeled images as either 'cat' or 'dog'. Which type of machine learning should the data scientist use?

Accepted Answer

Supervised learning. Supervised learning, because the dataset contains labeled images (each image is tagged as 'cat' or 'dog'), and the goal is to train a model to predict the label for new unlabeled images. Supervised learning algorithms, such as convolutional neural networks (CNNs), learn a mapping from input features (pixel values) to output labels using the provided ground-truth labels, enabling accurate classification on unseen data.

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Unsupervised learning

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Reinforcement learning

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Semi-supervised learning

Question 8

A business analyst wants to quickly summarize the main topics discussed in a large collection of customer feedback emails. The analyst needs to identify recurring concepts such as 'product quality', 'shipping delay', and 'customer service'. They want to use a prebuilt Azure AI Language feature without any custom training. Which feature should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct choice because it is a prebuilt Azure AI Language feature designed to automatically identify and return the main topics, concepts, and recurring themes from unstructured text, such as 'product quality' or 'shipping delay'. Unlike custom-trained models, this feature requires no training data and works out-of-the-box, making it ideal for quickly summarizing large collections of customer feedback emails.

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Named Entity Recognition (NER)

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Language Detection

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Sentiment Analysis

Question 9

A real estate agency wants to create a feature on their website that automatically crops uploaded property photos to focus on the house itself, removing excess sky, ground, or other surroundings. Which Azure Computer Vision capability should they use?

Accepted Answer

Smart cropping. Smart cropping is the correct capability because it uses AI to identify the most visually salient region of an image and automatically crops it to focus on the main subject—in this case, the house—while removing irrelevant background like sky or ground. This is distinct from generic cropping as it leverages computer vision to detect the primary object and compositionally frame it.

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OCR (Optical Character Recognition)

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Image captioning

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Object detection

Question 10

A law firm needs to process thousands of legal contracts. They want to extract specific clauses (e.g., 'confidentiality', 'indemnity') and also identify the names of parties and monetary amounts mentioned. Which Azure AI Language feature(s) should they use together to achieve both tasks?

Accepted Answer

Custom text classification and named entity recognition. Custom text classification allows the law firm to train a model to identify specific clauses like 'confidentiality' and 'indemnity' by providing labeled examples. Named entity recognition (NER) can then extract predefined entities such as person names (parties) and monetary amounts from the text. Together, these two features address both custom clause detection and standard entity extraction.

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Key phrase extraction and sentiment analysis

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Entity linking and language detection

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Summarization and conversational language understanding

Question 11

What is the difference between a binary classification model and a multi-class classification model?

Accepted Answer

Binary classification predicts two outcomes; multi-class predicts three or more outcomes. Binary classification models are designed to predict exactly two possible outcomes (e.g., spam/not spam), while multi-class classification models predict three or more mutually exclusive classes (e.g., classifying images of cats, dogs, and birds). In Azure Machine Learning, binary classification algorithms like Logistic Regression output a single probability score, whereas multi-class algorithms like Multinomial Logistic Regression or One-vs-Rest meta-estimators output a probability distribution across all classes.

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Binary classification uses numeric outputs; multi-class uses categorical outputs

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Binary is for images; multi-class is for text

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Binary classification is always more accurate than multi-class

Question 12

A developer wants to use Azure OpenAI to build a customer service chatbot that can answer questions about a company's return policy. They create a set of example question-answer pairs in the prompt without retraining the model. Which technique is being used?

Accepted Answer

Few-shot learning. Few-shot learning is the correct technique because the developer provides a small set of example question-answer pairs directly in the prompt to guide the model's responses, without retraining or updating the model's weights. This leverages the model's pre-existing knowledge to generalize from the examples, which is a hallmark of few-shot prompting in Azure OpenAI.

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Fine-tuning

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Reinforcement learning

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Transfer learning

Question 13

A news agency receives thousands of articles daily from wire services. They want to automatically identify and tag mentions of people, organizations, and locations within each article to improve search and categorization. The agency has no labeled training data. Which built-in Azure AI Language feature should they use?

Accepted Answer

Named Entity Recognition (NER). Named Entity Recognition (NER) is the correct choice because it is specifically designed to identify and categorize mentions of people, organizations, locations, and other entity types in unstructured text. Since the agency has no labeled training data, NER's pre-trained model can be used out-of-the-box without any custom training, making it ideal for automatically tagging articles with these entity types to improve search and categorization.

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Sentiment Analysis

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Key Phrase Extraction

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Language Detection

Question 14

A healthcare organization uses an AI system to predict patient readmission risk. The model was trained on data from a single hospital with a predominantly elderly population. When deployed to a different hospital with a younger demographic, the model's accuracy drops significantly. Which responsible AI principle is most directly violated?

Accepted Answer

Reliability and safety. The model's accuracy drop when applied to a different demographic indicates a failure in reliability and safety. The model was trained on a non-representative dataset (elderly patients) and does not generalize to younger populations, violating the principle that AI systems must perform consistently and safely across intended deployment contexts.

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Transparency

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Fairness

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Privacy and security

Question 15

What is 'Azure AI Speech Studio' and what does it help you do?

Accepted Answer

A no-code web portal for testing and configuring Azure AI Speech capabilities. Azure AI Speech Studio is a no-code web portal that allows you to test, configure, and integrate Azure AI Speech capabilities—such as speech-to-text, text-to-speech, custom voice, and speech translation—without writing code. It provides a graphical interface to experiment with prebuilt models, tune recognition accuracy, and generate sample code for deployment, making it ideal for rapid prototyping and evaluation of speech workloads.

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A recording studio application for producing AI-generated music and audio

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A professional audio editing tool for removing background noise from recordings

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An IDE extension that adds speech commands to control code editors