AI-900 Practice Exam 53 — 20 Questions

Question 1

A hospital uses an AI system to analyze patient records for research. To protect patient identities, the system should not store or transmit any personally identifiable information (PII) outside the secure network. Which responsible AI principle is most directly addressed by this requirement?

Accepted Answer

Privacy and Security. The requirement to not store or transmit PII outside the secure network directly addresses the Privacy and Security principle. This principle ensures that individuals' data is protected from unauthorized access or exposure, which is critical in healthcare AI systems handling sensitive patient records. By restricting PII to the secure network, the system upholds data confidentiality and compliance with regulations like HIPAA.

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Fairness

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Inclusiveness

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Reliability and Safety

Question 2

What is Azure Cognitive Search (Azure AI Search) and what role does it play in AI applications?

Accepted Answer

An enterprise search and retrieval service that powers RAG by indexing documents for semantic search. Azure Cognitive Search (now Azure AI Search) is a cloud-based enterprise search service that provides full-text search, vector search, and hybrid search capabilities. In AI applications, it plays a critical role in Retrieval Augmented Generation (RAG) by indexing documents and enabling semantic search, which allows AI models to retrieve relevant information from private data sources to ground their responses.

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A service for searching Azure subscription costs and billing records

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A web crawler that indexes public internet content like Bing

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A service for searching through Azure ML model training logs

Question 3

A financial institution uses an AI model to assess creditworthiness for loan applications. After deployment, they discover that the model assigns higher risk scores to applicants from certain postal codes, which are predominantly low-income minority neighborhoods. The model's predictions are accurate according to historical data, but the bank is concerned about ethical implications. Which Microsoft responsible AI principle is most directly applicable to addressing this issue?

Accepted Answer

Fairness. The model's assignment of higher risk scores based on postal codes, which correlate with low-income minority neighborhoods, directly violates the Fairness principle. This principle requires AI systems to treat all groups equitably and avoid reinforcing societal biases, even if the model's predictions are statistically accurate according to historical data. The bank's ethical concern centers on disparate impact, which fairness assessments (e.g., demographic parity or equal opportunity metrics) are designed to detect and mitigate.

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Inclusiveness

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Reliability and Safety

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Privacy and Security

Question 4

A manufacturer trains a model to detect defective parts on an assembly line. Only 2% of parts are defective. The model predicts 'non-defective' for all parts and achieves 98% accuracy. Which metric best reveals the model's inability to identify defective parts?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual defective parts correctly identified by the model. With 98% accuracy but zero true positives (since the model labels everything as non-defective), recall is 0%, which directly exposes the model's failure to detect any defective parts despite high accuracy.

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Accuracy

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Precision

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F1 Score

Question 5

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 6

A retail company wants to predict which customers are likely to cancel their subscription in the next 30 days. What ML task type is this?

Accepted Answer

Binary classification to predict whether each customer will cancel or stay. This is a binary classification task because the goal is to predict one of two mutually exclusive outcomes for each customer: either they will cancel (churn) or stay (not churn) within the next 30 days. Binary classification algorithms, such as logistic regression or decision trees, are specifically designed to assign each input to one of two discrete labels based on learned patterns from historical data.

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Clustering to identify similar customer segments

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Regression to predict the customer's lifetime value

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Generative AI to write personalized retention emails

Question 7

What is the Azure AI Face service's 'face verification' capability?

Accepted Answer

Comparing two facial images to determine if they belong to the same person. Azure AI Face service's 'face verification' capability is designed to compare two facial images and determine if they belong to the same person. It returns a confidence score and a boolean result indicating whether the faces match, based on a user-defined threshold. This is distinct from identification, which matches against a larger database.

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Confirming that detected faces belong to humans and not artificial representations

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Verifying that facial recognition results meet accuracy requirements

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Confirming the identity of a known person against a database of millions

Question 8

A data scientist trains a model on historical data and achieves high accuracy on both the training set and a held-out test set. However, when the model is deployed in production, it performs poorly on new, unseen data. Which issue is most likely the cause?

Accepted Answer

Data leakage. Data leakage occurs when information from outside the training dataset is inadvertently used to train the model, causing it to learn patterns that do not generalize to new data. In this scenario, the high accuracy on both training and test sets but poor production performance indicates that the test set was contaminated with information from the future or from the target variable, making the model appear accurate during validation but fail in real-world deployment.

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Overfitting

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Underfitting

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Concept drift

Question 9

What does Azure AI Language's 'language detection' feature return?

Accepted Answer

The language name, ISO code, and a confidence score. Azure AI Language's language detection feature returns the language name, its ISO 639-1 code, and a confidence score between 0 and 1. This allows applications to identify the language of input text with a quantifiable level of certainty, enabling downstream processing like routing to the correct language-specific model.

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Only the language name in English

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A translated version of the text in English

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Only a binary yes/no for whether the text is English

Question 10

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 11

A healthcare organization needs to extract specific data elements (such as patient names, medication dosages, and dates) from unstructured doctors' notes. Which Azure Cognitive Service is best suited for this task?

Accepted Answer

Text Analytics. Text Analytics (now part of Azure AI Language) is the correct service because it provides pre-built entity extraction capabilities specifically designed to identify and extract named entities like people (patient names), quantities (medication dosages), and dates from unstructured text. This aligns directly with the requirement to extract specific data elements from doctors' notes without needing custom model training.

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Language Understanding (LUIS)

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Translator Text

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Speech

Question 12

What is Azure Machine Learning's 'responsible AI dashboard'?

Accepted Answer

A multi-dimensional model analysis tool covering error analysis, interpretability, and fairness. The responsible AI dashboard in Azure Machine Learning is a comprehensive, multi-dimensional tool that integrates several open-source components (such as Error Analysis, InterpretML, and Fairlearn) to help data scientists and developers evaluate and improve their models across error analysis, interpretability, and fairness dimensions. It is designed to operationalize responsible AI practices by providing a single pane of glass for debugging model behavior, understanding feature importance, and detecting potential fairness issues.

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A legal compliance checklist for AI regulations in different countries

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A monitoring dashboard for tracking API usage and costs

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A tool for documenting model cards for AI transparency

Question 13

A manufacturing company wants to use Azure AI to detect surface defects on metal parts. The team has a small set of labeled images of defective and non-defective parts, and images will be taken under various lighting conditions and angles. They need a solution that can leverage a pre-trained model and adapt it to their specific defect types with minimal new training data. Which approach should they take?

Accepted Answer

A. Use Custom Vision to train a classification or object detection model with transfer learning. Option A is correct because Custom Vision allows you to use transfer learning, which starts from a pre-trained model and fine-tunes it on your small labeled dataset of defective and non-defective parts. This approach is ideal when you have limited training data and need to adapt the model to specific defect types under varying lighting and angles, as Custom Vision supports both classification and object detection for surface defects.

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B. Use the Optical Character Recognition (OCR) API

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C. Use the Describe Image API (Image Captioning)

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D. Use the Face API

Question 14

What is the 'frequency penalty' parameter in Azure OpenAI API calls?

Accepted Answer

A parameter that reduces repetition of words already present in the response. The 'frequency penalty' parameter in Azure OpenAI API calls is designed to reduce the likelihood of the model repeating words or phrases that have already appeared in the generated response. It works by applying a penalty proportional to the frequency of tokens already used, encouraging more diverse and less repetitive text output. This is distinct from the 'presence penalty', which penalizes tokens based on whether they have appeared at all, regardless of frequency.

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A cost multiplier based on how often you call the API

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A rate limiting parameter controlling maximum API calls per minute

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A filter that removes profanity based on how frequently it appears

Question 15

What is Azure AI Language's 'custom summarization' capability?

Accepted Answer

Fine-tuning the summarization model on domain-specific documents for improved specialized summaries. Azure AI Language's custom summarization allows you to fine-tune a pre-trained summarization model using your own domain-specific documents. This enables the model to generate more accurate and relevant summaries for specialized fields like legal, medical, or financial texts, rather than relying solely on generic training data.

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Generating summaries with custom fonts and formatting styles

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Setting a custom character limit for all generated summaries

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Automating summary creation for all documents in an Azure storage account

Question 16

A logistics company wants to automatically extract the tracking numbers, delivery addresses, and sender names from scanned shipping labels. Which prebuilt Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (OCR). Option B (Optical Character Recognition, or OCR) is correct because the task requires extracting text (tracking numbers, addresses, sender names) from scanned images. Azure Computer Vision's OCR API is specifically designed to detect and read printed or handwritten text from images, returning the text content along with bounding boxes. Object Detection, Image Classification, and Face Detection do not extract text, making OCR the appropriate prebuilt capability.

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Object Detection

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Image Classification

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Face Detection

Question 17

A medical research team needs to analyze thousands of clinical trial reports to extract specific medical terms like disease names, symptoms, and medications. They want to use an Azure AI Language feature that is pre-trained on medical data and requires no custom training. Which feature should they use?

Accepted Answer

Text Analytics for Health (Healthcare NLP). Option C is correct because Text Analytics for Health (Healthcare NLP) is a pre-trained Azure AI Language feature specifically designed to extract medical entities such as disease names, symptoms, medications, and treatment details from unstructured clinical text. It requires no custom training and is built on medical ontologies like UMLS, making it ideal for analyzing thousands of clinical trial reports without additional model development.

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Key Phrase Extraction

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Named Entity Recognition (NER)

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Sentiment Analysis

Question 18

A manufacturing company uses cameras on an assembly line to inspect products for defects such as scratches, dents, and discoloration. They need to identify the specific type of defect and its location on each product. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it not only identifies the presence of defects (like scratches, dents, or discoloration) but also localizes each defect by drawing bounding boxes around them. This meets the requirement to both classify the specific defect type and report its location on the product.

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Image classification

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Semantic segmentation

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Optical character recognition

Question 19

A startup develops an AI system that uses images of skin lesions to diagnose skin cancer. The model is trained exclusively on images from dermatology clinics in North America, which primarily feature lighter skin tones. When the system is deployed globally via a mobile app, it shows high accuracy for lighter skin tones but significantly lower accuracy for darker skin tones. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

B. Inclusiveness. The correct answer is B. Inclusiveness. The model was trained exclusively on images from North American dermatology clinics, which primarily feature lighter skin tones, leading to significantly lower accuracy for darker skin tones. This directly violates the inclusiveness principle, which requires AI systems to be designed for and perform well across all user groups, regardless of skin tone or other demographic characteristics.

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A. Reliability and Safety

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C. Privacy and Security

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D. Transparency

Question 20

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?

Accepted Answer

Speech to Text and Text Analytics for Health. Option B is correct because 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.

Answer

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