AI-900 Describe Artificial Intelligence workloads and considerations Practice Test 10 — 15 Questions

Question 1

What is prompt injection and why is it a security concern for AI systems?

Accepted Answer

A security attack where malicious inputs try to override AI system instructions or bypass guardrails. Prompt injection is a security attack where a malicious user crafts input that overrides the AI system's original instructions or bypasses its guardrails, causing the model to behave in unintended ways. This is a critical security concern because it can lead to data exfiltration, unauthorized actions, or the generation of harmful content, undermining the trust and safety of AI deployments.

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A technique for making AI prompts shorter and more efficient

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The process of adding examples to prompts to improve model performance

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A method of injecting training data into a model after deployment

Question 2

What is the 'AI Bill of Materials' (AI BOM) concept in responsible AI?

Accepted Answer

A transparency document listing all components (data, models, code) used in an AI system. The AI Bill of Materials (AI BOM) is a transparency document that lists all components—such as datasets, models, code, and dependencies—used in building an AI system. It is analogous to a software bill of materials (SBOM) and is a key practice in responsible AI to ensure traceability, reproducibility, and accountability. Option B correctly identifies this purpose.

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A financial document listing the costs of AI infrastructure components

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A checklist of billing items for Azure AI services

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A list of materials needed to build an AI chatbot interface

Question 3

What is 'model card' documentation in responsible AI?

Accepted Answer

Standardized documentation describing a model's intended use, performance, limitations, and ethical considerations. A model card is a standardized documentation framework, originally proposed by researchers at Google, that provides transparency about a machine learning model's intended use, performance metrics, limitations, and ethical considerations. This documentation helps stakeholders understand when and how to responsibly deploy the model, aligning with Microsoft's responsible AI principles of fairness, reliability, transparency, and accountability.

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A payment card system for purchasing AI cloud services

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A Flash card application for learning machine learning concepts

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A business card template for data scientists to share contact information

Question 4

What is 'knowledge mining' as an AI workload?

Accepted Answer

Using AI to extract meaningful information and insights from large volumes of unstructured content. Knowledge mining is an AI workload that uses services like Azure Cognitive Search to extract structured insights from unstructured data (documents, images, audio). It applies built-in AI skills (e.g., OCR, entity recognition, key phrase extraction) to index and enrich content, enabling search and discovery of hidden patterns.

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Mining cryptocurrency using AI-optimized algorithms

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A technique for extracting rare earth minerals used in GPU manufacturing

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Automatically generating training data from existing knowledge bases

Question 5

What does 'human-in-the-loop' data labeling mean in Azure Machine Learning?

Accepted Answer

Using ML to pre-label data while routing uncertain cases to human reviewers for quality assurance. In Azure Machine Learning, 'human-in-the-loop' data labeling combines ML model pre-labeling with human review for uncertain cases. This approach improves efficiency by automating easy labels while ensuring quality and accuracy through human oversight on ambiguous or low-confidence predictions, directly supporting active learning workflows.

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Replacing all human data labelers with ML models

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Requiring all data to be labeled by humans without any ML assistance

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Using a loop in Python code to automate the labeling process

Question 6

What is 'AI enrichment' in the context of Azure AI Search (Cognitive Search)?

Accepted Answer

Applying AI cognitive skills during search indexing to extract and enrich content with metadata. AI enrichment in Azure AI Search refers to the process of applying built-in or custom cognitive skills during the indexing pipeline to extract, transform, and enrich unstructured data (e.g., images, text, PDFs) with additional metadata. This enables capabilities such as OCR, entity recognition, key phrase extraction, and language detection, turning raw content into searchable, structured information without requiring separate ML training.

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Adding premium features to an Azure AI subscription

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Training custom ML models to improve search result ranking

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Encrypting indexed search content with AI-managed keys

Question 7

What is 'computer vision' and which tasks does it encompass?

Accepted Answer

The AI field enabling machines to interpret images and video — covering classification, detection, segmentation, and OCR. Computer vision is a subfield of AI that enables machines to derive meaningful information from digital images, videos, and other visual inputs. It encompasses a broad range of tasks including image classification (labeling an entire image), object detection (locating and classifying multiple objects), image segmentation (pixel-level partitioning), and optical character recognition (OCR) for text extraction. Option B correctly captures this full scope, making it the right answer.

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Computer vision only includes OCR and text extraction from documents

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Computer vision is exclusively used for medical imaging diagnosis

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The field of designing displays and cameras for computers

Question 8

What is the ethical concern with using AI for 'predictive policing'?

Accepted Answer

Potential to perpetuate racial bias, undermine due process, and create discriminatory self-fulfilling prophecies. Predictive policing AI systems often rely on historical crime data, which can contain inherent biases from over-policing in minority communities. This can lead to a feedback loop where the AI predicts more crime in those areas, prompting more police presence, which in turn generates more arrests and reinforces the original bias. Such systems also risk undermining due process by making decisions based on statistical correlations rather than individual evidence, and can create self-fulfilling prophecies where predicted crime hotspots become actual crime hotspots due to increased enforcement.

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Predictive policing AI is too expensive to implement at city scale

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Predictive policing models are too slow to be useful for real-time decisions

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Predictive policing AI might predict crimes in the wrong ZIP code

Question 9

What is 'AI transparency' and why is it challenging for deep learning models?

Accepted Answer

Deep learning models are 'black boxes' — high performance but difficult to explain because of millions of interacting parameters. Deep learning models, particularly those with many layers and millions of parameters, operate as 'black boxes.' Their internal decision-making processes are highly complex and non-linear, making it extremely difficult to trace how specific inputs lead to particular outputs. This lack of interpretability is the core challenge of AI transparency in deep learning.

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Transparency is easy for all AI models because they use simple mathematical formulas

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Transparency only matters for AI systems used in consumer products

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Transparency is fully solved by showing the training data to stakeholders

Question 10

What is 'model interpretability' and why is it important in responsible AI?

Accepted Answer

Understanding and explaining why a model produces specific predictions to enable trust and auditing. Model interpretability refers to the ability to understand and explain why a model produces specific predictions. It is a critical component of responsible AI because it enables trust, accountability, and auditing by allowing stakeholders to verify that decisions are fair, unbiased, and based on relevant features rather than spurious correlations.

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The ability to translate a model's code into multiple programming languages

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The speed at which a model processes inference requests

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The accuracy of a model as measured on a standard benchmark dataset

Question 11

Which of the following is an example of 'anomaly detection' as an AI workload?

Accepted Answer

Automatically identifying fraudulent credit card transactions that deviate from a customer's normal patterns. Anomaly detection identifies data points that deviate significantly from the norm. In this case, fraudulent credit card transactions are detected because they do not match the customer's typical spending patterns, which is a classic use case for anomaly detection in AI workloads.

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Translating customer support emails from Spanish to English

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Generating product descriptions from a list of specifications

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Classifying customer reviews as positive or negative

Question 12

What is 'data drift' and why is it a concern for deployed AI models?

Accepted Answer

The gradual change in data distribution over time that causes deployed model accuracy to degrade. Data drift refers to the gradual change in the statistical properties of the input data that a deployed AI model receives, compared to the data it was trained on. This shift in distribution causes the model's predictions to become less accurate over time because the model was optimized for the original data patterns. In Azure Machine Learning, data drift is monitored using dataset monitors that compare baseline and target datasets to detect significant changes, triggering retraining pipelines to maintain model performance.

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When training data files are accidentally moved to the wrong storage container

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The movement of data between Azure regions for latency optimisation

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Intentional modification of training data to improve model robustness

Question 13

What is 'natural language processing' (NLP) as a category of AI workload?

Accepted Answer

Using AI to process and understand text and speech in human languages. Natural language processing (NLP) is an AI workload that focuses on enabling computers to interpret, understand, and generate human language in both text and speech forms. It combines computational linguistics with statistical machine learning models to perform tasks like sentiment analysis, language translation, and speech recognition. This makes option A the correct definition.

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Programming computers using natural spoken language instead of code

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A network protocol for low-latency language model inference

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Automatically converting speech to a natural-sounding language

Question 14

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 15

What is 'explainable AI' (XAI) and why is it required in regulated industries?

Accepted Answer

AI systems that can explain their decisions in understandable terms — required for regulatory compliance. Explainable AI (XAI) refers to AI systems that provide human-understandable justifications for their decisions, predictions, or recommendations. In regulated industries such as finance, healthcare, and insurance, regulations like GDPR's 'right to explanation' and the EU AI Act require that automated decisions be transparent and auditable, making XAI a compliance necessity.

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AI systems with publicly available source code that anyone can inspect

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AI models that are simple enough for non-experts to rebuild from scratch

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AI systems that automatically explain errors in user-submitted code