AI-900 Practice Test 11 — 15 Questions

Question 1

What is 'regularization' in machine learning and why is it used?

Accepted Answer

Adding a complexity penalty to the training objective to reduce overfitting. Regularization is a technique used to reduce overfitting by adding a penalty term to the loss function during training. This penalty discourages the model from learning overly complex patterns (e.g., large weights) that fit the training data too closely but fail to generalize to new data. In Azure Machine Learning, regularization can be applied via algorithms like Lasso (L1) or Ridge (L2) regression, which directly modify the optimization objective.

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Normalizing input data to a standard scale before training

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Ensuring models comply with AI regulations in different jurisdictions

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Standardizing the format of training data from different sources

Question 2

Drag and drop the steps to deploy a model as a real-time inference endpoint in Azure Machine Learning into the correct order.

Accepted Answer

Register the model, then create the scoring script, then deploy the model to a compute target. Deploying a real-time endpoint requires registering the model, creating a scoring script, and deploying to compute.

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Create the scoring script, then register the model, then deploy the model to a compute target

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Register the model, then deploy the model to a compute target, then create the scoring script

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Deploy the model to a compute target, then register the model, then create the scoring script

Question 3

What is the question answering feature in Azure AI Language used for?

Accepted Answer

Building knowledge bases that automatically answer questions from FAQ content. The question answering feature in Azure AI Language is designed to extract answers from structured content like FAQs, manuals, or support documents. It builds a knowledge base that can automatically respond to user queries in natural language, making it ideal for customer support or self-service portals.

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Training custom language models for complex conversations

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Generating new questions from a given text

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Testing the quality of chatbot responses

Question 4

What is Azure AI Translator's 'custom translator' service?

Accepted Answer

A service for training custom machine translation models on domain-specific parallel texts. Azure AI Translator's 'custom translator' service allows users to build and train custom machine translation models using parallel documents (source-target sentence pairs) specific to a domain, such as legal or medical terminology. This improves translation accuracy for specialized content beyond what the general-purpose neural machine translation engine provides.

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A service that hires human translators to manually translate documents

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A feature that lets users customize the output language font and formatting

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A tool for automatically detecting which language content should be translated into

Question 5

A healthcare company deploys an AI system to assist doctors in diagnosing skin conditions from images. The system is a deep neural network that does not provide explanations for its predictions. The company implements a process where every AI recommendation is logged, and a medical team reviews any adverse outcomes to determine if the system or a human made an error. The company also clearly assigns responsibility for the system's outputs to a specific clinical oversight committee. Which Microsoft responsible AI principle is most directly being implemented by these actions?

Accepted Answer

Accountability. The actions described—logging AI recommendations, reviewing adverse outcomes to determine error source, and assigning a clinical oversight committee—directly implement the Accountability principle. Accountability requires that organizations clearly assign responsibility for AI system outputs and have processes to review and address impacts, which is exactly what the company does by creating a human review loop and a designated committee.

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Fairness

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Reliability and safety

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Transparency

Question 6

A logistics company wants to use AI to automatically sort packages based on their destination address printed on the label. Which AI workload combination is needed?

Accepted Answer

OCR to read the printed address and classification to determine routing. The scenario requires two distinct AI workloads: OCR (Optical Character Recognition) to extract the printed destination address from the package label, and classification to map that address to the correct routing category (e.g., zip code, region, or delivery route). This combination directly solves the problem of reading unstructured text and assigning it to a predefined output class.

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Reinforcement learning to optimize package sorting speed

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Facial recognition to identify the delivery person

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Sentiment analysis to assess package condition

Question 7

What is 'Azure AI Vision's image analysis v4.0' and what new capability does it add?

Accepted Answer

Florence-powered advanced capabilities including dense captioning, embeddings, and improved background removal. Azure AI Vision's image analysis v4.0 is a major update that leverages the Florence foundation model to deliver advanced capabilities such as dense captioning (generating detailed descriptions for multiple regions in an image), image embeddings (vector representations for similarity search), and improved background removal. This version significantly enhances the depth and accuracy of image understanding compared to previous versions.

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A version supporting 4K resolution images for the first time

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A version requiring 4x more compute than the previous version

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The fourth iteration of Microsoft's Kinect 3D depth sensor SDK

Question 8

What does Azure AI Vision's 'optical character recognition' (OCR) feature do?

Accepted Answer

Extracts printed and handwritten text from images and documents. Azure AI Vision's OCR feature is designed to extract printed and handwritten text from images and documents, converting visual text into machine-readable data. This is correct because OCR uses deep learning models to detect and read text characters from various visual sources, enabling downstream processing like search or analysis.

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Converts text files into images for archival purposes

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Recognises optical fibre cables in data centre photographs

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Corrects spelling errors in text extracted from forms

Question 9

What is the Azure Machine Learning model registry?

Accepted Answer

A centralized repository for versioning, tracking, and managing trained ML models. The Azure Machine Learning model registry is a centralized repository within Azure Machine Learning that enables versioning, tracking, and management of trained machine learning models. It allows data scientists and MLOps engineers to register models with metadata, tags, and descriptions, and to manage multiple versions of the same model, facilitating reproducibility, collaboration, and deployment lifecycle management.

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A marketplace for purchasing pre-built AI models

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A compliance database for AI regulatory requirements

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A system for monitoring models in production for data drift

Question 10

What is the GPT-4o model in Azure OpenAI?

Accepted Answer

A multimodal model that natively processes text, images, and audio inputs and outputs. GPT-4o is a multimodal model in Azure OpenAI that natively processes and generates text, images, and audio inputs and outputs. Unlike earlier GPT-4 versions that required separate models or pipelines for different modalities, GPT-4o integrates these capabilities into a single unified model, enabling richer interactions such as analyzing an image and responding with spoken audio.

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A text-only model optimized for faster response speeds than GPT-4

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A model specialized for generating only programming code

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An older, less capable version of GPT-4

Question 11

Which Azure AI service can detect the language of a text input and return the identified language name and confidence score?

Accepted Answer

Azure AI Language (language detection). Azure AI Language's language detection feature is specifically designed to identify the language of a text input, returning both the language name and a confidence score between 0 and 1. This capability is part of the Natural Language Processing (NLP) workloads within Azure AI Language, making option B the correct choice for this task.

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Azure AI Vision

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Azure AI Translator

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Azure AI Document Intelligence

Question 12

What is the bias-variance tradeoff in machine learning?

Accepted Answer

The balance between model simplicity (underfitting) and model complexity (overfitting). The bias-variance tradeoff directly addresses the tension between underfitting (high bias, overly simple model) and overfitting (high variance, overly complex model). In Azure Machine Learning, this tradeoff is managed through hyperparameter tuning (e.g., regularization strength, tree depth) to achieve optimal generalization on unseen data.

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Choosing between model accuracy and computational cost

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Deciding whether to use biased training data or unbiased test data

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The tradeoff between training speed and model size

Question 13

A fashion retailer wants to automatically generate new, unique images of clothing items based on textual descriptions (e.g., 'a blue silk dress with floral patterns'). Which Azure service would be most appropriate to accomplish this?

Accepted Answer

B) Azure OpenAI Service. Azure OpenAI Service provides access to powerful generative AI models like GPT-4 and DALL-E, which can create new images from textual descriptions. This service is specifically designed for generative tasks, such as producing unique clothing images based on prompts like 'a blue silk dress with floral patterns', making it the most appropriate choice.

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A) Azure Machine Learning

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C) Azure Cognitive Search

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D) Custom Vision

Question 14

A developer uses Azure OpenAI Service to generate marketing copy. They want the model to produce more focused and deterministic responses, reducing the variety of outputs for the same prompt. Which parameter should the developer decrease?

Accepted Answer

Temperature. Temperature controls the randomness of the model's output. Lowering temperature (e.g., from 1.0 to 0.2) makes the model more deterministic and focused, reducing output variety for the same prompt. This is the correct parameter to adjust for more consistent marketing copy.

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Max tokens

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Top P

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Frequency penalty

Question 15

A customer support team uses an AI chatbot to analyze incoming messages. They want to automatically identify the most frequently mentioned topics, such as 'shipping delay', 'refund policy', and 'product quality', without manually reading each message. Which Azure AI Language feature should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct choice because it automatically identifies the main topics and concepts in text, such as 'shipping delay', 'refund policy', and 'product quality', without requiring manual reading. This feature returns a list of key phrases that represent the most salient points in the input, making it ideal for topic frequency analysis in customer support messages.

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Language Detection

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Sentiment Analysis

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Entity Recognition