Which statement best captures the relationship between artificial intelligence, machine learning, and deep learning?

Deep learning is a subset of machine learning, which is itself a subset of artificial intelligence

Artificial intelligence is a narrow branch that sits entirely inside the field of machine learning

Deep learning is the broad umbrella term that fully contains machine learning and artificial intelligence

Machine learning and deep learning are unrelated fields that each compete against artificial intelligence

What does it mean when a large language model 'hallucinates'?

It produces fluent, confident output that is actually factually incorrect or fabricated

It slows down noticeably because the input prompt it received was simply far too long

It stores the user's private personal data permanently without ever obtaining any consent

It refuses outright to answer any question that happens to involve numbers or arithmetic

A company wants to convert written text into natural-sounding speech for an accessibility feature. Which service fits?

Amazon Polly, which synthesizes lifelike spoken audio from written text

Amazon Comprehend, which analyzes the meaning and sentiment of text

Amazon Transcribe, which converts recorded or streaming speech into written text

Amazon Rekognition, which analyzes objects and scenes in images and video

What is transparency in the context of responsible AI?

Being open about how the system works, its data, its limits, and where it is used

Deliberately making the model physically invisible

Guaranteeing that the deployed model can never be inspected or reviewed by any person at all

Actively hiding the model's training data from auditors and regulators

What is a hyperparameter in machine learning?

A configuration value, such as learning rate, that is set before training and is not learned by the model itself

A value the model learns automatically from the data during training

The final prediction the model outputs at inference time

A single column of input data fed to the model

The Top P (nucleus sampling) inference parameter controls what?

Sampling from the smallest set of tokens whose cumulative probability reaches a threshold, affecting output diversity

The maximum number of tokens allowed in the response

The AWS Region that is used to run inference

Whether the model's weights are updated during the request

Why does token count matter when using a large language model?

Tokens determine how much text fits in the context window and typically drive usage-based pricing

Tokens are the encryption keys that secure the model

Tokens are the model's internal learned weights

Token count has no effect on either cost or capacity

Which AWS service can serve as the vector store backing an Amazon Bedrock knowledge base?

Amazon Transcribe speech-to-text

In which situation is prompt engineering usually sufficient, making fine-tuning unnecessary?

When the task can be handled by clearly instructing the model and optionally giving a few examples

When you must permanently embed many thousands of proprietary domain facts directly into the model itself

When the model must adopt an entirely new language it never saw in training

When only a full retraining could possibly meet the requirement

Designing an AI system so operators can guide, override, or shut it down when needed reflects which responsible-AI dimension?

Explainability and interpretability

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1. Fundamentals of AI and ML
Which statement best captures the relationship between artificial intelligence, machine learning, and deep learning?
- a.Artificial intelligence is a narrow branch that sits entirely inside the field of machine learning
- b.Deep learning is the broad umbrella term that fully contains machine learning and artificial intelligence
- c.Machine learning and deep learning are unrelated fields that each compete against artificial intelligence
- d.Deep learning is a subset of machine learning, which is itself a subset of artificial intelligence
Answer: d
Explanation: AI is the broad goal of machines performing tasks that seem intelligent. Machine learning is a subset of AI in which systems learn patterns from data. Deep learning is a further subset of ML that uses multi-layer neural networks.
2. Fundamentals of Generative AI
What does it mean when a large language model 'hallucinates'?
- a.It slows down noticeably because the input prompt it received was simply far too long
- b.It stores the user's private personal data permanently without ever obtaining any consent
- c.It produces fluent, confident output that is actually factually incorrect or fabricated
- d.It refuses outright to answer any question that happens to involve numbers or arithmetic
Answer: c
Explanation: A hallucination is generated content that sounds plausible and confident but is factually wrong or invented. It is a core reliability risk of LLMs and a reason to ground outputs in verified sources and to review them.
3. Applications of Foundation Models
A company wants to convert written text into natural-sounding speech for an accessibility feature. Which service fits?
- a.Amazon Comprehend, which analyzes the meaning and sentiment of text
- b.Amazon Transcribe, which converts recorded or streaming speech into written text
- c.Amazon Rekognition, which analyzes objects and scenes in images and video
- d.Amazon Polly, which synthesizes lifelike spoken audio from written text
Answer: d
Explanation: Amazon Polly is text-to-speech, turning written text into lifelike audio. Transcribe does the reverse (speech-to-text), Comprehend analyzes text meaning, and Rekognition analyzes images and video.
4. Guidelines for Responsible AI
What is transparency in the context of responsible AI?
- a.Deliberately making the model physically invisible
- b.Guaranteeing that the deployed model can never be inspected or reviewed by any person at all
- c.Being open about how the system works, its data, its limits, and where it is used
- d.Actively hiding the model's training data from auditors and regulators
Answer: c
Explanation: Transparency means clearly communicating how a system operates, what data it uses, its limitations, and where and how it is applied, so users, auditors, and regulators can make informed judgments. It is the opposite of concealment.
5. Fundamentals of AI and ML
What is a hyperparameter in machine learning?
- a.A value the model learns automatically from the data during training
- b.A configuration value, such as learning rate, that is set before training and is not learned by the model itself
- c.The final prediction the model outputs at inference time
- d.A single column of input data fed to the model
Answer: b
Explanation: Hyperparameters (learning rate, number of layers, etc.) are set before training and control how learning happens. Parameters such as weights are what the model actually learns from data during training.
6. Fundamentals of Generative AI
The Top P (nucleus sampling) inference parameter controls what?
- a.The maximum number of tokens allowed in the response
- b.The AWS Region that is used to run inference
- c.Sampling from the smallest set of tokens whose cumulative probability reaches a threshold, affecting output diversity
- d.Whether the model's weights are updated during the request
Answer: c
Explanation: Top P restricts sampling to the most probable tokens that together reach a cumulative probability p, balancing diversity and focus. It never updates weights and is separate from the max-tokens length limit.
7. Fundamentals of Generative AI
Why does token count matter when using a large language model?
- a.Tokens determine how much text fits in the context window and typically drive usage-based pricing
- b.Tokens are the encryption keys that secure the model
- c.Tokens are the model's internal learned weights
- d.Token count has no effect on either cost or capacity
Answer: a
Explanation: Text is processed as tokens; the number of tokens affects both how much content fits in the context window and how much you pay under per-token pricing. Tokens are units of text, not keys or weights.
8. Applications of Foundation Models
Which AWS service can serve as the vector store backing an Amazon Bedrock knowledge base?
- a.Amazon Polly
- b.Amazon OpenSearch Serverless
- c.Amazon Transcribe speech-to-text
- d.AWS CloudTrail
Answer: b
Explanation: Amazon OpenSearch Serverless offers a vector engine that stores embeddings for similarity search behind a Bedrock knowledge base. Polly, Transcribe, and CloudTrail are unrelated to vector storage.
9. Applications of Foundation Models
In which situation is prompt engineering usually sufficient, making fine-tuning unnecessary?
- a.When you must permanently embed many thousands of proprietary domain facts directly into the model itself
- b.When the task can be handled by clearly instructing the model and optionally giving a few examples
- c.When the model must adopt an entirely new language it never saw in training
- d.When only a full retraining could possibly meet the requirement
Answer: b
Explanation: If a well-crafted prompt (optionally with a few examples) reliably produces the needed output, fine-tuning's added cost and effort are unnecessary. Deeply embedding large bodies of knowledge is where customization earns its cost.
10. Guidelines for Responsible AI
Designing an AI system so operators can guide, override, or shut it down when needed reflects which responsible-AI dimension?
- a.Controllability
- b.Fairness
- c.Explainability and interpretability
- d.Portability
Answer: a
Explanation: Controllability is the ability to steer, correct, or stop an AI system's behavior, an important safeguard. Explainability is about understanding decisions and fairness about equitable outcomes, which are separate dimensions.