AI-900 Practice Exam 57 — 20 Questions

Question 1

What does Azure AI Language's 'text analytics for health' feature provide?

Accepted Answer

Extracting structured clinical information from unstructured medical text. Azure AI Language's 'text analytics for health' feature is designed to extract structured clinical information—such as diagnoses, medications, symptoms, and procedures—from unstructured medical text like clinical notes, discharge summaries, and radiology reports. It uses specialized NLP models trained on medical ontologies (e.g., UMLS, ICD-10-CM) to identify and normalize healthcare entities, enabling downstream analytics and decision support.

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Real-time patient health monitoring from IoT medical devices

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Providing medical advice and treatment recommendations

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Monitoring the health status of Azure AI services

Question 2

A bank is developing an AI system to automatically approve or reject small business loan applications. The bank wants to ensure that the system does not unfairly discriminate against applicants based on their age, gender, or ethnicity. Which Microsoft responsible AI principle should most directly guide the design and evaluation of this system?

Accepted Answer

Fairness. The bank's goal is to prevent discrimination based on age, gender, or ethnicity in loan approvals. The Fairness principle directly addresses this by requiring AI systems to treat all groups equitably and to mitigate biases in training data and model predictions. This principle guides the design and evaluation of the system to ensure that outcomes are not skewed by protected attributes.

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Reliability and safety

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Privacy and security

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Inclusiveness

Question 3

A data scientist has a dataset containing information about houses: size (sq ft), number of bedrooms, location, and the actual sale price. The goal is to train a model that predicts the price of a new house based on these features. Which type of machine learning task is this?

Accepted Answer

B) Regression. This is a regression task because the goal is to predict a continuous numeric value (the sale price) based on input features. Regression models learn the relationship between independent variables (size, bedrooms, location) and a dependent variable (price) to output a real number. In Azure Machine Learning, regression algorithms like Linear Regression, Decision Forest Regression, or Neural Network Regression would be appropriate for this scenario.

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A) Classification

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C) Clustering

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D) Reinforcement Learning

Question 4

A data scientist trains a deep neural network on a small dataset. The model achieves 100% accuracy on the training data but only 60% accuracy on a validation set. Which technique is most appropriate to address this issue?

Accepted Answer

Apply regularization. The model's perfect training accuracy (100%) paired with poor validation accuracy (60%) is a classic sign of overfitting, where the model has memorized the training data rather than learning generalizable patterns. Regularization techniques (e.g., L1/L2 regularization, dropout) penalize large weights or randomly drop neurons during training, which forces the network to learn simpler, more robust features and reduces overfitting on small datasets.

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Increase the number of training epochs

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Add more hidden layers

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Increase the learning rate

Question 5

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). 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 6

What is the Azure AI Speech service's 'speaker diarization' feature?

Accepted Answer

Identifying and labeling different speakers in a multi-speaker audio recording. Speaker diarization is the process of partitioning an audio stream into homogeneous segments according to the speaker identity. It answers the question 'who spoke when?' by assigning a unique label (e.g., Speaker 1, Speaker 2) to each segment in a multi-speaker recording. This is a built-in capability of Azure AI Speech, often used in meeting transcription and call center analytics.

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Converting a speaker's voice to a different language in real time

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Measuring the speaking speed (words per minute) of each speaker

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Filtering out background speakers from a primary speaker's recording

Question 7

Which Azure service provides a no-code/low-code drag-and-drop interface for building machine learning pipelines?

Accepted Answer

Azure Machine Learning Designer. Azure Machine Learning Designer is the correct answer because it provides a drag-and-drop, no-code/low-code visual interface for building, testing, and deploying machine learning pipelines. Users can connect pre-built modules for data transformation, model training, and scoring without writing code, making it ideal for rapid prototyping and operationalization of ML workflows.

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Azure AI Custom Vision

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Azure AI Language Studio

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Azure Databricks

Question 8

A company uses Azure OpenAI Service to generate marketing copy for social media posts. They want to prevent the model from producing content that contains offensive language, harmful stereotypes, or violent themes that go against their brand guidelines. Which feature should the company configure within Azure OpenAI Service?

Accepted Answer

Configuring the content filtering (responsible AI filters). B is correct because Azure OpenAI Service includes built-in content filtering (responsible AI filters) that automatically detects and blocks offensive language, harmful stereotypes, and violent themes in both input prompts and generated outputs. This feature enforces brand guidelines without requiring custom model modifications or manual oversight.

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Fine-tuning the model with a custom dataset

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Increasing the token limit per response

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Using prompt engineering techniques

Question 9

A retail company uses overhead cameras to monitor shelf inventory in a store. They want to build a system that automatically detects whether a shelf section is empty or stocked, and specifically identify product categories (e.g., 'soft drinks', 'chips', 'canned goods') and count the number of items in each category. The company has a large set of labeled images showing different shelf states. Which Azure Computer Vision service should they use to build this custom detection and counting solution?

Accepted Answer

Custom Vision object detection. Custom Vision object detection is the correct choice because it allows the company to train a model on their labeled images to detect and localize specific product categories (e.g., 'soft drinks', 'chips') and count items within each category. Unlike pre-built Computer Vision features, Custom Vision enables custom object detection with bounding boxes and classification, which directly supports the requirement for detecting shelf states and counting items per category.

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Computer Vision Image Analysis with dense captioning

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Optical Character Recognition (OCR)

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Azure Machine Learning with a pre-trained YOLO model

Question 10

A customer support team wants to automatically analyze thousands of product reviews. Their goal is to extract the most frequently mentioned topics (e.g., 'battery life', 'customer service', 'screen quality') without manually reading each review. 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 automatically identifies the main points or topics (e.g., 'battery life', 'customer service', 'screen quality') from unstructured text. This directly meets the requirement to extract frequently mentioned topics from thousands of product reviews without manual reading.

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Sentiment analysis

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Language detection

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Entity recognition

Question 11

A data scientist is training a binary classification model to detect rare equipment failures from sensor data. The dataset contains 99.5% normal operation readings and only 0.5% failure readings. The model currently predicts all readings as 'normal' and achieves 99.5% accuracy on the test set. The business requires the model to identify at least 80% of actual failures. Which data-level technique should the data scientist use to most directly address the class imbalance?

Accepted Answer

Oversample the minority class (failure examples). Oversampling the minority class (failure examples) directly addresses the severe class imbalance by creating synthetic copies or duplicates of the rare failure instances. This balances the training dataset, allowing the model to learn patterns associated with failures rather than always predicting the majority class. With a balanced dataset, the model can be trained to meet the business requirement of identifying at least 80% of actual failures, even though overall accuracy may decrease.

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Undersample the majority class (normal examples)

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Use precision as the optimization metric

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Reduce the complexity of the model

Question 12

A data scientist trains a regression model on a dataset with 100 features and 10,000 samples. The model achieves a low training error but a much higher error on a held-out test set. Which approach is most likely to improve the model's generalization performance?

Accepted Answer

Reduce the number of features or apply regularization. The model exhibits high variance (overfitting), as indicated by low training error but high test error. Reducing the number of features or applying regularization (e.g., L1/L2) directly constrains model complexity, forcing it to learn more general patterns rather than memorizing noise. This is the standard approach to improve generalization in regression models.

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Increase the complexity of the model by adding more layers or parameters

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Add more training data

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Use a different train-test split ratio like 80-20 instead of 70-30

Question 13

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

Accepted Answer

A flat penalty discouraging repetition of any token already present in the response. The 'presence penalty' parameter in Azure OpenAI API calls applies a flat penalty to any token that has already appeared in the response so far, reducing the model's likelihood of repeating that token. This helps generate more diverse and less repetitive text by discouraging the reuse of tokens already present in the output sequence.

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A parameter requiring AI systems to acknowledge their presence as AI to users

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A parameter indicating whether the AI is present online or offline

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The minimum number of characters that must be present in a response

Question 14

A data scientist trains a classification model on a dataset of 10,000 labeled emails to distinguish spam from non-spam. The model achieves 99% accuracy on the training data but only 70% accuracy on a held-out test set. Which term best describes this situation?

Accepted Answer

B) Overfitting. The model performs exceptionally well on training data (99% accuracy) but poorly on unseen test data (70% accuracy), which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set rather than generalizing to new data, often due to excessive model complexity or insufficient regularization.

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A) Underfitting

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C) Bias-variance tradeoff

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D) Regularization

Question 15

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 16

What is 'Azure AI Vision's Read API' and what makes it superior for OCR?

Accepted Answer

An advanced OCR service handling multi-page PDFs, handwriting, and complex layouts with word-level coordinates. Azure AI Vision's Read API is an advanced OCR service that extracts text from images and documents, including multi-page PDFs, handwritten text, and complex layouts. It is superior because it returns word-level bounding box coordinates and confidence scores, enabling precise text localization and structured output for downstream processing.

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The standard API for reading data from Azure Storage accounts and databases

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An API for reading audio content and converting it to text transcripts

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A feature for reading the metadata of image files stored in Azure Blob Storage

Question 17

A customer support team wants to use Azure AI Language to automatically analyze incoming support emails. They need to extract the product name mentioned in each email and determine whether the customer's sentiment is positive, negative, or neutral. They have no labeled data for custom training. Which two prebuilt Azure AI Language features should they use together?

Accepted Answer

Named entity recognition (NER) and sentiment analysis. Named entity recognition (NER) extracts specific entities like product names from text, while sentiment analysis determines the emotional tone (positive, negative, neutral). Both are prebuilt, no-code features in Azure AI Language that require no labeled data for custom training, making them the correct pair for this use case.

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Key phrase extraction and language detection

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Conversational language understanding (CLU) and translation

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Text summarization and personal identifying information (PII) detection

Question 18

What is the difference between 'training' and 'inference' in machine learning?

Accepted Answer

Training creates models from data; inference uses trained models to make predictions. Training is the phase where a machine learning model learns patterns from labeled or unlabeled data by adjusting its internal parameters (e.g., weights in a neural network) to minimize a loss function. Inference is the subsequent phase where the trained model applies those learned patterns to new, unseen data to generate predictions or classifications. In Azure Machine Learning, training typically involves running a script on a compute target (e.g., a GPU cluster) and registering the resulting model, while inference is performed by deploying that model as a real-time endpoint or batch pipeline.

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Training is for testing models; inference is for training them

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They are the same process with different names for clarity

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Training is for image models; inference is for text models

Question 19

A hotel booking website wants to automatically analyze guest-submitted photos of hotel rooms to verify if they contain common amenities such as a bed, a desk, and a chair. They want to use a prebuilt Azure AI service without any custom training. Which feature should they use?

Accepted Answer

Object Detection. Object Detection (prebuilt) is the correct choice because it can identify and locate multiple specific objects (bed, desk, chair) within an image by drawing bounding boxes around them. This prebuilt Azure AI Vision feature requires no custom training and directly supports detecting common amenities in hotel room photos.

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Optical Character Recognition (OCR)

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Image Analysis (prebuilt)

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Handwriting OCR

Question 20

A marketing team uses Azure OpenAI Service to generate marketing copy. They notice the generated text is often repetitive, using the same phrases and words multiple times. Which parameter should they increase to directly reduce this repetition?

Accepted Answer

Frequency penalty. Frequency penalty directly reduces repetition by penalizing tokens that have already appeared in the generated text. A higher frequency penalty value (e.g., 0.5 to 1.0) decreases the likelihood of the model reusing the same phrases or words, making the output more diverse and less repetitive.

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Temperature

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Top-p

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Max tokens