AI-900 Practice Exam 65 — 20 Questions

Question 1

A customer support team receives emails in multiple languages. They want to automatically determine the language of each email and then extract key phrases to summarize the issue. Which two Azure AI Language features should they use in sequence?

Accepted Answer

Language detection and entity extraction. Option C is correct because the scenario requires first identifying the language of each email (using Language Detection) and then extracting key phrases from the text to summarize the issue (using Key Phrase Extraction). These two features are designed to work sequentially in Azure AI Language, where language detection provides the language code needed for key phrase extraction to operate accurately.

Answer

Sentiment analysis and key phrase extraction

Answer

Entity extraction and sentiment analysis

Question 2

A language teacher uses Azure AI Language to automatically analyze hundreds of student essays. The teacher wants to identify the main topics discussed in each essay and also understand the overall sentiment (positive, negative, or neutral) expressed. Which two prebuilt Azure AI Language features should the teacher use together to accomplish this goal?

Accepted Answer

Key phrase extraction and Sentiment analysis. Key phrase extraction identifies the main topics discussed in each essay by extracting salient terms and phrases, while sentiment analysis determines the overall sentiment (positive, negative, or neutral) expressed in the text. Together, these two prebuilt Azure AI Language features directly address the teacher's goal of analyzing both topics and sentiment.

Answer

Entity recognition and Language detection

Answer

Text summarization and Key phrase extraction

Question 3

A marketing team uses Azure OpenAI to generate product descriptions. They want the output to reflect their latest catalog and current pricing, not the model's general knowledge. Which technique should they use?

Accepted Answer

Retrieval Augmented Generation (RAG). Retrieval Augmented Generation (RAG) is the correct technique because it allows the model to retrieve up-to-date information from an external knowledge base—such as the latest catalog and current pricing—and incorporate that data into the generated output. Unlike the model's static training data, RAG dynamically injects fresh, domain-specific content at inference time, ensuring accuracy and relevance without modifying the model itself.

Answer

Few-shot learning

Answer

Fine-tuning

Answer

Prompt engineering

Question 4

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 5

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. Option C is correct because 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 6

A company deploys an AI system to screen job resumes and rank candidates. The company wants to ensure that candidates can understand how the system arrived at its decisions. Which Microsoft responsible AI principle is most directly addressed by this requirement?

Accepted Answer

Transparency. The requirement that candidates can understand how the AI system arrived at its decisions directly aligns with the Transparency principle, which mandates that AI systems be interpretable and that their decision-making processes be explainable to users. In the context of resume screening, this means providing clear reasoning for why a candidate was ranked a certain way, such as highlighting which features (e.g., skills, experience) most influenced the score.

Answer

Fairness

Answer

Reliability and Safety

Answer

Privacy and Security

Question 7

A bank deploys an AI system to approve loan applications. The system was trained on historical data that contains systematic biases against certain ethnic groups. Despite awareness of this bias, the bank proceeds with deployment, expecting the system to correct itself over time. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Fairness. The bank knowingly deployed an AI system trained on biased historical data, expecting it to self-correct. This directly violates the Fairness principle, which requires AI systems to treat all groups equitably and avoid discrimination. Microsoft's responsible AI framework mandates that biases be actively identified and mitigated before deployment, not left to chance.

Answer

Reliability and safety

Answer

Transparency

Answer

Privacy and security

Question 8

A self-driving car company tests its AI navigation system in a new city. The system fails to detect a temporary construction barrier and causes a collision. The company wants to ensure that their AI system is robust to unexpected and unusual environmental conditions. Which Microsoft responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Reliability and safety. The requirement is to ensure the AI system is robust to unexpected and unusual environmental conditions, which directly falls under the responsible AI principle of Reliability and safety. This principle focuses on building systems that operate consistently and safely under a wide range of conditions, including edge cases like temporary construction barriers, and that fail gracefully when they cannot perform as expected.

Answer

Fairness

Answer

Privacy and security

Answer

Transparency

Question 9

A developer uses Azure OpenAI Service to generate code snippets. They need the model to produce the most likely completion each time, with no randomness or creativity. Which parameter should they set?

Accepted Answer

temperature = 0. Setting temperature = 0 forces the model to always select the token with the highest probability at each step, eliminating randomness and ensuring deterministic, most-likely completions. This is ideal for tasks like code generation where consistency and predictability are required, as it disables the sampling randomness that higher temperature values introduce.

Answer

temperature = 1

Answer

top_p = 0.5

Answer

frequency_penalty = 0.5

Question 10

A library wants to automatically generate descriptive alt text for hundreds of historical photographs in their digital archive. For each photo, the system should produce a natural-language description that includes objects present (e.g., 'a horse', 'a carriage'), the action being performed (e.g., 'pulling'), and the scene type (e.g., 'city street'). Which Azure Computer Vision capability should they use?

Accepted Answer

Image Analysis (Describe image). The Image Analysis 'Describe image' capability is designed to generate human-readable captions that summarize the content of an image, including objects, actions, and scene context. This directly matches the library's requirement to produce natural-language descriptions for historical photographs, as it uses a combination of object detection and scene understanding to output a full sentence.

Answer

Optical Character Recognition (OCR)

Answer

Object detection

Answer

Face detection

Question 11

A marketing team uses Azure OpenAI Service to generate product descriptions. They have a base description and want the model to produce multiple variations with different tones, such as formal, playful, and technical, while still being factually accurate. Which parameter should they adjust to control the randomness and diversity of the output?

Accepted Answer

temperature. Temperature controls the randomness of the model's output by scaling the logits before applying the softmax function. A higher temperature (e.g., 0.8) increases diversity and creativity, while a lower temperature (e.g., 0.2) makes the output more deterministic and focused. For generating product descriptions with different tones while maintaining factual accuracy, adjusting temperature is the correct approach.

Answer

max_tokens

Answer

top_p

Answer

frequency_penalty

Question 12

A university deploys an AI model to predict which students are at risk of dropping out. The predictions are used to offer targeted support. Students who may be negatively impacted by this prediction have the right to understand how the model arrived at its decision. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Transparency. Transparency is the responsible AI principle that requires AI systems to be understandable and interpretable. In this scenario, students have the right to know how the model arrived at its dropout prediction, which directly aligns with transparency's goal of providing clear explanations for AI decisions. This principle ensures that affected individuals can access meaningful information about the logic and factors used by the model.

Answer

Fairness

Answer

Reliability and safety

Answer

Privacy and security

Question 13

An insurance company uses an AI system to automatically process and approve or reject claims. The system sometimes rejects valid claims because the uploaded documents are in slightly different formats (e.g., PDF vs. scanned images). The company wants to minimize these errors. Which Microsoft responsible AI principle is most directly relevant to addressing this issue?

Accepted Answer

Reliability and safety. The issue is that the AI system fails to process valid claims due to variations in document formats (PDF vs. scanned images), which is a reliability and safety problem. The system should be robust enough to handle input variations and consistently produce correct outcomes. Microsoft's Reliability and safety principle focuses on ensuring AI systems operate reliably, safely, and consistently under expected conditions, directly addressing the need to minimize such errors.

Answer

Fairness

Answer

Inclusiveness

Answer

Transparency

Question 14

What is the 'Azure OpenAI Playground' and what is it used for?

Accepted Answer

A web-based interface for interactively testing Azure OpenAI models and prompts without coding. The Azure OpenAI Playground is a web-based interface that allows users to interactively test and experiment with Azure OpenAI models (like GPT-4, GPT-3.5, and DALL-E) by entering prompts and adjusting parameters (e.g., temperature, max tokens) without writing any code. It is used for rapid prototyping, prompt engineering, and evaluating model behavior before integrating into applications via the API.

Answer

A children's educational game powered by Azure OpenAI for learning to code

Answer

A sandboxed environment for running untrusted AI models safely

Answer

A feature for generating synthetic training data for custom model fine-tuning

Question 15

A company develops an AI system to recommend personalized news articles to users. The system uses collaborative filtering, suggesting articles that similar users have read. Which type of machine learning does this approach primarily rely on?

Accepted Answer

Unsupervised learning. Collaborative filtering identifies patterns in user-item interactions without labeled outcomes, grouping users or items based on similarity. This is a classic unsupervised learning task because the system discovers hidden structures (e.g., user clusters) from unlabeled data, rather than being trained on explicit input-output pairs.

Answer

Supervised learning

Answer

Reinforcement learning

Answer

Semi-supervised learning

Question 16

Which Azure AI service is purpose-built for detecting anomalies in time series data?

Accepted Answer

Azure AI Anomaly Detector. Azure AI Anomaly Detector is a dedicated, pre-built service specifically designed to identify anomalies in time series data without requiring custom model training. It uses machine learning algorithms to automatically detect spikes, dips, or pattern changes in sequential data, making it the correct choice for this purpose.

Answer

Azure Machine Learning with custom anomaly detection models

Answer

Azure AI Language sentiment analysis

Answer

Azure AI Vision spatial analysis

Question 17

Which responsible AI principle ensures that AI systems work reliably across different conditions and for all users, including those from different demographics?

Accepted Answer

Reliability and safety. The Reliability and safety principle ensures that AI systems perform consistently and correctly under a wide range of conditions, including edge cases and diverse demographic groups. This principle requires rigorous testing, validation, and monitoring to prevent failures or biased outcomes that could harm users. In the context of AI-900, this principle directly addresses the need for systems to work reliably for all users, regardless of age, gender, ethnicity, or other demographic factors.

Answer

Privacy

Answer

Transparency

Answer

Accountability

Question 18

A data scientist trains a binary classification model to detect fraudulent transactions. The dataset contains 99% legitimate transactions (negative class) and 1% fraudulent transactions (positive class). The model predicts 'legitimate' for every transaction in the test set and achieves 99% accuracy. Which metric would best reveal that the model is failing to identify any fraudulent transactions?

Accepted Answer

Recall. Recall (also known as sensitivity or true positive rate) measures the proportion of actual positive cases (fraudulent transactions) that the model correctly identifies. With 99% accuracy but zero true positives, the recall is 0%, which immediately reveals the model's complete failure to detect fraud. In Azure Machine Learning, the classification metrics pane would show recall = 0.0 for the positive class, highlighting this issue despite high accuracy.

Answer

Accuracy

Answer

Precision

Answer

F1-score

Question 19

A bank is developing an AI system to automatically approve or reject small personal loans. To ensure the system treats applicants fairly regardless of race, gender, or age, which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Fairness. The Fairness principle is directly relevant because it requires AI systems to treat all individuals equitably, avoiding discrimination based on protected attributes like race, gender, or age. In this loan approval scenario, the system must be designed and tested to ensure its decisions do not systematically disadvantage any group, which is the core goal of fairness in AI.

Answer

Inclusiveness

Answer

Reliability and safety

Answer

Transparency

Question 20

A logistics company receives thousands of handwritten shipping labels each day. They want to use Azure AI to automatically read the handwritten addresses and convert them into digital text. Which Azure Cognitive Services capability should they use?

Accepted Answer

Optical character recognition (OCR). Optical character recognition (OCR) is the correct Azure Cognitive Services capability because it is specifically designed to extract printed or handwritten text from images and convert it into machine-readable digital text. In this scenario, the logistics company needs to read handwritten addresses from shipping labels, which is a classic OCR workload. Azure's Computer Vision OCR API (including the Read API) can handle both printed and handwritten text, making it the ideal choice for this task.

Answer

Image classification

Answer

Object detection

Answer

Face detection