AI-900 Practice Test 27 — 15 Questions

Question 1

What is 'AI-assisted labelling' in Azure Machine Learning data labelling?

Accepted Answer

Using a partially trained model to pre-populate labels that human annotators verify and correct. AI-assisted labelling in Azure Machine Learning uses a partially trained model to automatically suggest labels for unlabelled data. Human annotators then review and correct these suggestions, which speeds up the labelling process while maintaining quality. This is a form of active learning where the model iteratively improves as more labelled data is verified.

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Automatically generating descriptive captions for images using a pre-trained model

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Deploying a model to production without any human review of its outputs

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Using AI to detect and remove incorrectly labelled examples from a completed dataset

Question 2

A data scientist has a dataset with 100 features and 10,000 samples. They want to reduce the number of features while retaining as much variance as possible, to improve model training speed and reduce overfitting. Which technique should they use?

Accepted Answer

Principal Component Analysis (PCA). Principal Component Analysis (PCA) is an unsupervised dimensionality reduction technique that transforms the original features into a new set of orthogonal components, ordered by the amount of variance they capture. By selecting only the top principal components, the data scientist can significantly reduce the feature count (e.g., from 100 to 20) while retaining the majority of the dataset's variance, which directly improves model training speed and reduces overfitting.

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Feature scaling

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Regularization

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Cross-validation

Question 3

What is the purpose of Microsoft's 'Responsible AI Impact Assessment'?

Accepted Answer

A pre-deployment framework for identifying and mitigating potential AI harms. The Responsible AI Impact Assessment is a pre-deployment framework designed to help organizations identify, document, and mitigate potential harms associated with AI systems before they are released. It aligns with Microsoft's responsible AI principles, such as fairness, reliability, privacy, and transparency, ensuring that risks are systematically addressed rather than measured after deployment.

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A performance benchmark measuring AI response times

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A financial model for calculating AI project ROI

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A testing framework for measuring AI model accuracy

Question 4

A hospital wants to automatically extract patient symptoms and medication names from clinical notes. They have a set of pre-defined categories for symptoms and medications, and they have manually labeled a few hundred sentences to indicate which text spans belong to each category. Which Azure AI Language feature should they use to build this custom entity extraction solution?

Accepted Answer

Custom entity extraction. Custom entity extraction (D) is the correct choice because the hospital needs to identify specific text spans (symptoms and medication names) based on their own pre-defined categories, using a small set of manually labeled sentences for training. This is exactly what Azure's custom named entity recognition (NER) feature does—it allows you to train a model to extract custom entities from unstructured text, tailored to your domain-specific labels.

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Pre-built entity recognition

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Key phrase extraction

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Custom text classification

Question 5

What is 'curriculum learning' and how does it relate to training stability?

Accepted Answer

Training models on progressively harder examples to improve stability and convergence. Curriculum learning is a training strategy where a model is first exposed to simpler examples and then gradually introduced to more complex ones. This approach improves training stability by preventing the model from being overwhelmed by difficult patterns early on, which can cause large gradient updates and divergence. By structuring the learning process, the model converges more reliably and often achieves better generalization.

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Designing a course curriculum using AI to personalise learning for students

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A structured plan for the sequence of ML courses a data scientist should take

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Using a pre-defined curriculum of hyperparameter values to systematically explore the search space

Question 6

A logistics company scans thousands of packages daily. They need an automated system to read handwritten shipping labels to sort packages correctly. Which Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR), because the scenario requires extracting handwritten text from images of shipping labels to automate sorting. OCR is the specific Azure Computer Vision capability designed to detect and read printed or handwritten text from images, returning machine-readable text that can be used for downstream processing.

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Image Analysis (descriptions and tags)

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Object Detection

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Face API

Question 7

A legal firm needs to automatically extract case-specific entities such as 'docket number', 'plaintiff attorney', and 'court name' from legal documents. They have a small set of manually labeled examples for each entity. Which Azure AI Language feature should they use to build this custom entity extraction solution?

Accepted Answer

Custom named entity recognition (NER). Custom named entity recognition (NER) allows you to train a model with your own labeled examples to extract domain-specific entities like 'docket number' and 'plaintiff attorney'. Prebuilt entity extraction only recognizes common, generic entities (e.g., person, location) and cannot be customized for legal case-specific terms. This makes custom NER the correct choice for building a tailored extraction solution with a small set of manually labeled data.

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Prebuilt entity extraction

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Key phrase extraction

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Sentiment analysis

Question 8

A multinational company needs to automatically translate customer support emails from English to Spanish and French. The emails are plain text. Which Azure service should they use?

Accepted Answer

Azure AI Translator. Azure AI Translator is the correct service because it provides real-time text translation between multiple languages, including English to Spanish and French, via a REST API. It is specifically designed for plain text translation tasks, making it ideal for automatically translating customer support emails without requiring audio processing or conversational AI.

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Azure AI Language (Sentiment Analysis)

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Azure AI Speech

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Azure Bot Service

Question 9

What is 'automated machine learning' (AutoML) in Azure Machine Learning?

Accepted Answer

Automatically iterating through algorithms and hyperparameters to find the best model for a dataset. Automated machine learning (AutoML) in Azure Machine Learning automates the process of selecting the best machine learning algorithm and tuning its hyperparameters for a given dataset. It iterates through multiple combinations of algorithms and hyperparameter values, evaluating each model's performance to identify the optimal solution without manual intervention. This is why option B is correct.

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A system that automatically retrains models on a fixed daily schedule

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Automatically labelling training data using existing model predictions

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A robot that physically connects GPU hardware for distributed training

Question 10

What is 'structured output' (JSON mode) in Azure OpenAI?

Accepted Answer

Constraining model responses to valid JSON conforming to a specified schema for application integration. Structured output (JSON mode) in Azure OpenAI constrains the model to generate responses that are valid JSON objects conforming to a user-defined schema. This is achieved by setting the `response_format` parameter to `{ "type": "json_object" }` and optionally providing a JSON schema via the `json_schema` parameter, ensuring the output can be directly parsed and integrated into applications without additional formatting logic.

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Formatting the model's text response with numbered sections and bullet points

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Saving model responses to a structured database table automatically

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Generating output in multiple languages simultaneously in a structured format

Question 11

What is 'overfitting' in machine learning and how does Azure ML help prevent it?

Accepted Answer

When a model learns training data too specifically and fails to generalise to new data. Overfitting occurs when a machine learning model learns the training data too precisely, including noise and outliers, resulting in poor performance on unseen data. Azure ML helps prevent overfitting through automated machine learning (AutoML) which applies regularization, cross-validation, and early stopping techniques, as well as by enabling easy configuration of train/test splits and hyperparameter tuning.

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When a model is trained on too much data and becomes too accurate

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When a model's predictions exceed the acceptable numerical range

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When Azure ML runs training for longer than the allocated compute budget

Question 12

What industries benefit most from Azure AI Document Intelligence's capabilities?

Accepted Answer

Finance, healthcare, legal, government, and any industry processing high volumes of documents. Azure AI Document Intelligence (formerly Form Recognizer) is designed to extract, analyze, and structure data from documents at scale using prebuilt and custom models. Industries like finance, healthcare, legal, and government process massive volumes of forms, invoices, medical records, and contracts, making them the primary beneficiaries of automated document processing.

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Only the entertainment industry for processing movie scripts

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Only manufacturing for quality control inspection

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Only retail for product catalog management

Question 13

What is Azure AI Content Safety used for in computer vision scenarios?

Accepted Answer

Detecting harmful or inappropriate content in images for content moderation. Azure AI Content Safety is designed to detect harmful or inappropriate content in images, such as violence, hate speech, self-harm, or sexually explicit material. In computer vision scenarios, it analyzes visual features to classify content into severity levels, enabling automated content moderation. This directly supports safe user-generated content platforms by flagging or blocking prohibited imagery.

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Compressing images to reduce storage costs

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Enhancing image quality and resolution

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Converting images to text descriptions for accessibility

Question 14

A company uses Azure OpenAI to generate marketing copy. They want to ensure that the generated text does not contain inappropriate or harmful content before it is published. Which Azure OpenAI feature is specifically designed for this purpose?

Accepted Answer

Content filters. Content filters are the Azure OpenAI feature specifically designed to detect and block inappropriate or harmful content in generated text. They apply configurable severity levels across categories like hate, violence, self-harm, and sexual content, ensuring outputs meet safety policies before publication.

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Temperature

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Top-p (nucleus sampling)

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System message

Question 15

What is a neural network?

Accepted Answer

A machine learning model architecture with layers of interconnected nodes that learn representations. A neural network is a machine learning model architecture composed of layers of interconnected nodes (neurons) that process input data through weighted connections and activation functions. These layers learn hierarchical representations of data, enabling the model to capture complex patterns and relationships without explicit rule-based programming. This aligns with option B as the correct definition.

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A computer network for distributed AI training across multiple servers

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A database for storing trained ML models

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A rule-based expert system for decision making