Pass the Microsoft Microsoft Certified: Azure AI Fundamentals AI-901 Questions and answers with CertsForce

AI systems must consistently operate as intended, even under unexpected conditions. = Reliability and safety

This description matches the Microsoft responsible AI principle of reliability and safety, which focuses on ensuring that AI systems behave consistently, safely, and as expected, including under unusual or unexpected conditions.

AI systems must protect and secure personal and business information. = Privacy and security

This description matches the Microsoft responsible AI principle of privacy and security, which focuses on protecting personal, confidential, and business-sensitive information and preventing unauthorized access or misuse.

A schema defines which fields to extract when analyzing content.

In Azure Content Understanding, the schema or fieldSchema defines the structured data that the analyzer extracts from content, including field names, types, and extraction behavior. Microsoft documentation states that Content Understanding lets you define a schema to extract, classify, or generate field values from unstructured content.

The other options are incorrect:

A keyword list does not define the complete structured output fields.

OCR-only processing extracts text, but it does not define structured fields.

A synchronous API call describes a request pattern, not the extraction schema.

The correct answer is classify email messages as work-related or personal. Natural language processing (NLP) is concerned with analyzing, interpreting, and processing human language, including unstructured text contained in documents, messages, reviews, and emails. Text classification is therefore a direct NLP workload: an AI model analyzes the linguistic content of an email and assigns it to a predefined category such as work-related or personal.

Microsoft ' s current AI-901 study guide explicitly requires candidates to identify scenarios for common AI workloads, including text analysis, and to understand common text-analysis techniques. Microsoft Azure Language in Foundry Tools similarly supports capabilities for extracting information from text and classifying text, demonstrating that categorization based on textual content is an NLP function.

Predicting future car rentals is primarily a regression/forecasting problem because the output is a numerical quantity. Predicting whether a website visitor will transact is typically a binary classification machine-learning problem based on behavioral or transactional features, not necessarily language. Stopping industrial machinery when a temperature threshold is exceeded is a rule-based monitoring/control scenario.

The completed sentence is:

When developing an AI system for self-driving cars, the Microsoft reliability and safety principle for responsible AI should be applied to ensure consistent operation of the system during use.

Self-driving cars are safety-critical AI systems. The key requirement in the question is consistent operation of the system during use, which maps directly to the Microsoft responsible AI principle of reliability and safety.

The other options are incorrect:

Fairness focuses on reducing bias and treating people equitably.

Accountability focuses on ensuring people remain responsible for AI systems and their outcomes.

Inclusiveness focuses on designing AI systems that work for and empower diverse users, including people with different needs or abilities.

Therefore, the correct answer is reliability and safety.

GPT and DALL-E are examples of generative AI model types/families.

GPT models generate natural language text, such as answers, summaries, emails, and conversations.

DALL-E models generate images from text descriptions.

Why the other options are incorrect:

A. transformer is a model architecture, not the best answer here as a generative model type/family.

B. computer vision is an AI workload area.

E. natural language processing is an AI workload area, not a specific generative AI model type.

Statement

Answer

Image analysis capabilities in Microsoft Foundry Tools can perform optical character recognition (OCR).

Yes

Image analysis capabilities in Microsoft Foundry Tools can generate captions and descriptive tags for images.

Yes

Image analysis capabilities in Microsoft Foundry Tools are designed primarily to create new images from text prompts.

No

Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide/topics:

The correct sequence is Yes, Yes, No.

The first statement is Yes. Azure Vision Image Analysis in Microsoft Foundry Tools supports optical character recognition (OCR). Microsoft documents Image Analysis 4.0 as supporting synchronous OCR through its Read capability, which extracts printed or handwritten text contained within images.

The second statement is also Yes. Image Analysis provides both captioning and image tagging. Captions generate a human-readable description of image content, while tags identify recognizable objects, scenes, actions, and other visual concepts. Microsoft explicitly lists capabilities such as Captions, Dense Captions, Tags, Object Detection, People detection, and Read/OCR as Image Analysis features.

The third statement is No. Image Analysis is primarily concerned with extracting information from existing images, not generating new images. Creating an image from a textual prompt is a generative AI/image-generation workload, typically performed by image-generation models. Image Analysis instead interprets visual input by extracting text, captions, tags, objects, and related information.

Fairness

Privacy and security

Transparency

Reliability and safety

Comprehensive and Detailed Explanation with all Azure AI documents: =

Evaluating model outputs to ensure that decisions are NOT biased against specific demographic groups: Fairness

Microsoft’s responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias across demographic groups.

Encrypting sensitive customer data and restricting system access to authorized personnel: Privacy and security

The privacy and security principle requires AI systems to protect personal and sensitive data and prevent unauthorized access.

Informing users when they are interacting with an AI system and explaining the system’s capabilities and limitations: Transparency

The transparency principle requires that people understand when they are interacting with AI and understand the system’s purpose, capabilities, and limitations.

Testing AI systems under different conditions to reduce unexpected failures: Reliability and safety

The reliability and safety principle means AI systems should operate reliably and safely, including through testing and validation under expected and unexpected conditions.

Named Entity Recognition (NER) is used to extract dates, quantities, and locations from text.

Named Entity Recognition identifies and categorizes entities in text, such as people, organizations, locations, dates, times, quantities, currencies, and other structured information.

The other options are incorrect:

Key phrase extraction identifies important phrases or main topics.

Language detection identifies the language of the text.

Sentiment Analysis identifies positive, negative, neutral, or mixed sentiment.

Azure Content Understanding analysis operations are asynchronous long-running operations. Microsoft’s Python SDK documentation states that analysis starts with a begin operation, then the application polls for results until analysis completes.

Therefore, after submitting the PDF for analysis, the application should poll the operation results until the asynchronous analysis job succeeds.