AI-900 Practice Test 5 — 10 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