AI-900 Practice Exam 3 — 20 Questions

Question 1

A city government implements an AI system to analyze traffic camera feeds and predict congestion. The system is found to be less accurate for neighborhoods with lower-income populations because historical traffic data from those areas is sparse. Which Microsoft responsible AI principle is most directly relevant to address this issue?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Fairness. The system's reduced accuracy for lower-income neighborhoods due to sparse historical data is a direct fairness issue. Fairness in AI requires that systems perform equitably across different demographic groups, and this scenario describes a clear disparity in model performance based on socioeconomic factors. Addressing this would involve techniques like data augmentation, reweighting, or collecting more representative data to mitigate bias.

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Transparency

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Accountability

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Privacy and security

Question 2

What is the 'dual-use' problem in AI and why is it relevant to responsible deployment?

Accepted Answer

The risk that AI capabilities designed for good can also be used for harmful purposes. The 'dual-use' problem in AI refers to the risk that a technology designed for beneficial purposes can also be misapplied for harmful ends. This is central to responsible deployment because it forces organizations to consider not only the intended use case but also potential misuse, such as facial recognition systems used for surveillance or generative AI creating disinformation. Addressing dual-use requires implementing safeguards like usage policies, access controls, and ethical review boards.

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When an AI model is licensed for use by two different organisations simultaneously

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Deploying the same AI model for both training and inference to reduce costs

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Combining two AI models to achieve better results than either model alone

Question 3

What ethical consideration is MOST important when deploying AI systems for hiring decisions?

Accepted Answer

Auditing for and mitigating bias that could disadvantage protected demographic groups. The most critical ethical consideration in AI-driven hiring is fairness and non-discrimination. AI systems can inadvertently learn and amplify historical biases present in training data, leading to unfair outcomes for protected groups under laws like Title VII of the Civil Rights Act. Auditing for and mitigating bias ensures the AI model's decisions are equitable and legally compliant, which is a core principle of responsible AI.

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Ensuring the AI processes applications as quickly as possible

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Making the AI the final decision-maker for all candidates

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Ensuring the AI is only deployed in large companies

Question 4

What is 'ROC-AUC' and when is it a better metric than accuracy for classification?

Accepted Answer

A threshold-agnostic metric that measures discrimination ability — better than accuracy for imbalanced classes. ROC-AUC (Receiver Operating Characteristic - Area Under the Curve) is a threshold-agnostic metric that measures a model's ability to discriminate between positive and negative classes across all possible classification thresholds. It is a better metric than accuracy when dealing with imbalanced classes because accuracy can be misleadingly high if the model simply predicts the majority class, whereas ROC-AUC evaluates the trade-off between true positive rate and false positive rate independently of class distribution.

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ROC-AUC is always better than accuracy regardless of the use case

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A metric specifically for measuring multi-class classification across more than two classes

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An evaluation metric only applicable to models trained on Azure Machine Learning

Question 5

What is 'Microsoft Semantic Kernel' and how does it relate to Azure OpenAI?

Accepted Answer

Microsoft's open-source SDK for orchestrating LLMs with plugins, memory, and planning. Microsoft Semantic Kernel is an open-source SDK that enables developers to orchestrate large language models (LLMs) like Azure OpenAI by integrating plugins, memory, and planning capabilities. It abstracts the complexity of chaining AI calls, managing context, and executing multi-step tasks, making it a core tool for building generative AI workloads on Azure.

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A low-level kernel module that optimises GPU utilisation for Azure OpenAI inference

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A tool for evaluating the semantic accuracy of Azure OpenAI model responses

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Microsoft's proprietary alternative to Azure OpenAI for internal use only

Question 6

What is 'Azure AI Foundry' and how does it relate to building enterprise AI applications?

Accepted Answer

Microsoft's enterprise platform for the full AI development lifecycle — models, evaluation, deployment, and governance. Azure AI Foundry is Microsoft's unified enterprise platform that supports the entire AI development lifecycle, from model selection and fine-tuning to evaluation, deployment, and governance. It integrates Azure AI services, model catalog, prompt flow, and responsible AI tools, enabling teams to build, manage, and monitor production AI applications at scale. This makes it the correct answer for how Azure AI Foundry relates to building enterprise AI applications.

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A physical manufacturing facility that produces Azure AI hardware accelerators

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A certification programme for Azure AI engineers who build production AI systems

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An open-source framework for building AI pipelines outside of Azure

Question 7

What is anomaly detection in the context of AI workloads?

Accepted Answer

Identifying data points that deviate significantly from expected patterns. Anomaly detection is an AI technique that identifies data points, events, or observations that deviate significantly from the majority of the data or from expected patterns. In AI workloads, this is typically implemented using statistical methods, clustering algorithms (like k-means), or neural networks (e.g., autoencoders) to flag outliers for further investigation. Option B correctly captures this core definition, as anomaly detection is fundamentally about finding deviations, not about classification, grammar, or duplication.

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Classifying images into categories of 'normal' and 'abnormal'

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Detecting grammatical errors in text

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Finding duplicate records in a database

Question 8

A social media platform wants to automatically detect and flag images that contain violent content or adult material before they are published. Which prebuilt Azure Computer Vision capability should they use?

Accepted Answer

Image Analysis (with content moderation). Azure Computer Vision's Image Analysis includes a content moderation feature that can detect adult, racy, and violent content in images. This prebuilt capability is specifically designed to flag inappropriate material before publication, making it the ideal choice for the social media platform's requirement.

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Optical Character Recognition (OCR)

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Object Detection

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Background Removal

Question 9

What is 'translation quality estimation' and how does Azure AI Translator use AI for it?

Accepted Answer

AI-predicted quality scores for translations without requiring human reference translations. Translation quality estimation uses AI to predict a quality score for a machine translation output without needing a human-written reference translation. Azure AI Translator leverages neural networks to analyze the source and translated text, producing a confidence score that indicates how reliable the translation is, which helps users decide whether to use the output directly or send it for human review.

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Estimating how long it will take a human translator to review machine translations

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Customer satisfaction surveys about the quality of Azure AI Translator's output

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A quota system limiting low-quality languages to fewer translation requests

Question 10

A law firm needs to automatically extract specific information from legal contracts, such as the names of the parties involved, effective dates, and governing law clauses. The firm has a small set of contracts that have been manually annotated with these specific fields. Which Azure AI Language feature should they use to build a custom extraction solution?

Accepted Answer

Custom Named Entity Recognition (NER). Custom Named Entity Recognition (NER) is the correct choice because the law firm needs to extract specific, custom fields (party names, effective dates, governing law clauses) from legal contracts, which are not covered by prebuilt entity categories. Custom NER allows you to train a model using a small set of manually annotated contracts to recognize these domain-specific entities, enabling tailored extraction for the firm's unique requirements.

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Prebuilt Named Entity Recognition (NER)

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Key Phrase Extraction

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Text Summarization

Question 11

A logistics company uses overhead cameras at a shipping dock to read labels on packages. The labels contain text in various fonts, sizes, and orientations, and sometimes the text is partially obscured. Which Azure Computer Vision capability should they use to extract the text from these labels?

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR) is the correct choice because it is specifically designed to extract printed or handwritten text from images, handling variations in fonts, sizes, orientations, and partial occlusion. Azure Computer Vision's OCR API (Read API) uses deep-learning models to detect and digitize text from natural scenes, making it ideal for reading labels on packages in a logistics environment.

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Object detection

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Image classification

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Semantic segmentation

Question 12

What is the difference between face detection and face identification?

Accepted Answer

Face detection finds face locations; face identification determines who the person is from an enrolled database. Face detection is a computer vision task that locates human faces in an image or video, returning bounding box coordinates. Face identification (or recognition) goes a step further by matching a detected face against a database of enrolled individuals to determine a specific identity. Option B correctly distinguishes these two operations: detection finds where faces are, while identification determines who the person is.

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Face detection identifies who the person is; face identification counts how many faces are present

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Face detection works on videos; face identification works on static images only

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They are the same operation with different names

Question 13

What is the purpose of Azure AI Speech's 'batch transcription' capability?

Accepted Answer

Asynchronous processing of large volumes of audio files for cost-efficient transcription at scale. Azure AI Speech's batch transcription is designed for asynchronous processing of large volumes of pre-recorded audio files. It allows you to submit multiple audio files for transcription without requiring real-time interaction, making it cost-efficient for scenarios like call center analytics or media captioning where immediate results are not needed.

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Real-time transcription of live audio streams for immediate use

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Synchronizing speech transcription across multiple languages simultaneously

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Training a custom speech recognition model on audio samples

Question 14

What is 'label imbalance' in a classification dataset and how does it affect model training?

Accepted Answer

When one class greatly outnumbers others, causing models to be biased toward the majority class. Label imbalance refers to a situation in classification datasets where one class (the majority class) has significantly more samples than other classes (minority classes). This causes the model to become biased toward predicting the majority class, as it minimizes overall loss by ignoring minority classes, leading to poor generalization and low recall for underrepresented classes.

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When labels in the training data contain spelling errors

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When training labels are applied inconsistently by different human annotators

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When a model produces predictions that don't match any of the training labels

Question 15

What is the Azure AI Translator's 'document translation' capability?

Accepted Answer

Asynchronously translating complete documents while preserving layout and formatting. Azure AI Translator's document translation capability is designed for asynchronous batch translation of entire documents (e.g., Word, PDF, HTML) while preserving the original layout, structure, and formatting. This is achieved through the Document Translation API, which processes files in their entirety rather than translating individual text snippets in real time.

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Translating short text snippets from a chat interface

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Creating translated copies of database records

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Converting documents from one file format to another in a different language

Question 16

A data scientist is preparing a dataset to train a model that predicts customer churn. The dataset includes a column 'CustomerID' which is a unique identifier for each customer. Should the data scientist include the 'CustomerID' column as a feature in the training data?

Accepted Answer

No, because the CustomerID is a random unique identifier with no predictive power for churn.. CustomerID is a unique identifier that does not contain any meaningful pattern or relationship with the target variable (churn). Including such a column would introduce noise and risk overfitting, as the model could memorize each ID rather than learning generalizable patterns. In Azure Machine Learning, features should be predictive attributes, not arbitrary labels.

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Yes, because it uniquely identifies each customer and helps the model differentiate them.

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Yes, because the model can learn patterns from the numeric values.

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No, because the CustomerID column contains too many missing values.

Question 17

A parking lot management company uses security cameras to monitor vehicles. They need to both detect the presence of license plates in an image and read the alphanumeric characters on those plates. Which Azure Computer Vision capability should they use to achieve both requirements?

Accepted Answer

Optical Character Recognition (OCR) - Read API. (OCR - Read API) is correct because Azure's Read API is specifically designed to both detect the presence of text (including license plates) in an image and extract the alphanumeric characters from that text. This meets both requirements—detecting the plate and reading its characters—in a single call, using deep-learning-based recognition models optimized for printed and handwritten text.

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Image Analysis (describe image and detect objects)

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Face API

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Custom Vision (object detection)

Question 18

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.

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Fairness

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Privacy and security

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Transparency

Question 19

What does the Azure AI Foundry model catalog provide?

Accepted Answer

A curated collection of AI models from Microsoft and partners for evaluation and deployment. The Azure AI Foundry model catalog provides a curated collection of AI models from Microsoft and partners, including foundation models, industry-specific models, and open-source models like those from Hugging Face. This catalog enables users to evaluate, fine-tune, and deploy models directly within the Azure ecosystem, supporting generative AI workloads such as content generation and natural language processing.

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A library of pre-written Python code for common AI tasks

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A marketplace for purchasing training datasets from vendors

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A service for storing and versioning custom-trained models only

Question 20

A company wants to build a chatbot that can answer questions based on its internal policy documents. The documents are stored in Azure Blob Storage. They plan to use Azure OpenAI to generate answers. Which approach should they use to ensure the answers are grounded in the actual policy content?

Accepted Answer

Use Azure AI Search to index the documents and provide relevant passages as context to GPT-4. Azure AI Search can index the policy documents stored in Azure Blob Storage, enabling retrieval of relevant passages based on the user's query. These passages are then provided as context in the prompt to GPT-4, ensuring the generated answer is grounded in the actual policy content rather than relying on the model's pre-trained knowledge.

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Fine-tune GPT-4 on all policy documents

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Include the entire policy document text in the prompt each time

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Use DALL-E to visualize policy concepts