AWS Certified AI Practitioner Practice Exam

What is an availability zone (AZ)?

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A physical facility that houses numerous AWS servers

A virtual division of AWS infrastructure designed to ensure fault tolerance

A firewall-like security group that controls access to AWS resources

A geographical region that covers more than one continent

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Which cloud service model provides full applications to users without requiring them to manage infrastructure?

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Infrastructure as a service (IaaS)

Platform as a service (PaaS)

Software as a service (SaaS)

Virtualization as a service (VaaS)

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What is a defining characteristic of the hybrid cloud model?

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It exclusively uses private data centers for all workloads

It combines public and private cloud environments

It relies only on on-premises infrastructure

It uses a multitenant model for security

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What is a key advantage of AWS regions?

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They eliminate the need for availability zones (AZs)

They allow customers to comply with data residency requirements

They are only available in Asia

The provide unlimited computing power without redundancy

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Which AWS service allows developers to deploy applications without managing servers?

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Amazon EC2

AWS Lambda

Amazon RDS

Amazon CloudFront

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Which of the following best describes Amazon S3?

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A relational database service for structured data storage

A scalable object storage service designed for durability and availability

A high performance compute service for running applications

A content delivery network (CDN) for accelerating web traffic

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What is the main benefit of using a confusion matrix to evaluate a machine learning (ML) model?

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It shows the training time of the model

It helps analyze false positives and false negatives

It calculates the total number of data points used in training

It eliminates the need for additional performance metrics

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A bank wants to detect fraudulent transactions in real time. What type of inference should they use?

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Batch inference

Asynchronous inference

Real-time inference

On-demand inference

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What is a major challenge when using high-dimensional datasets in machine learning (ML)?

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It reduces the need for model tuning

It increases computational costs and complexity

It makes models interpret data more efficiently

It ensures better accuracy for all ML tasks

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An organization notices that its machine learning (ML) model is showing less accurate results over time due to changing customer behavior. What issue is this?

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Model overfitting

Hyperparameter tuning issue

Feature engineering error

Drift

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What is the goal for using Amazon SageMaker Model Monitor?

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To train deep-learning models faster

To automatically deploy machine learning (ML) models

To detect issues such as data drift and concept drift

To fine-tune pretrained models

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A business wants to improve efficiency of their data processing pipeline by automating feature extraction and transformation. Which AWS tool is best suited for this?

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Amazon Rekognition

Amazon Textract

Amazon SageMake Data Wangler

AWS Glue

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A software company wants to analyze customer feedback to determine wheter reviews are positive, neutral, or negative. Which AWS service should they use?

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Amazon Textract

Amazon Comprehend

AWS Lambda

Amazon SageMaker Feature Store

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What is the primary purpose of hyperparameter tuning in machine learning (ML)?

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To create new training data

To adjust model parameters for improved performance

To convert categorical data into numerical form

To speed up the training process

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How do diffusion models work?

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They add and remove noise in various steps

They use competing neural networks

They only create text

They only create sound

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What is the primary purpose of fine-tuning a foundation model (FM)?

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To make a model more general-purpose

To customize the model for a specific domain

To lower the latency

To train the model from scratch

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How does an encoder work in a transformer model?

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It creates synthetic data

It fine-tunes hyperparameters

It evaluates model predictions

It processes input sequences and extracts meaningful representations

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Which of the following describes a limit on how much a large language model (LLM) can process at a time?

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Overfitting

Bias

Context windows

The discriminator

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What's the reason for using reinforcement learning from human feedback (RLHF) in generative AI models?

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To improve the alignment the model's responses with human preferences

To lower training costs

To replace deep learning models

To reduce the latency of the model

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What is a key benefit of a multimodal foundation model (FM)?

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It requires much less data for the training

It can process and create different types of content like text, images, and videos

it is more explainable than text-based models

It does not require GPUs

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Of the choices below, which is a main reason why large language models consume significant compute resources?

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They use retrieval-augmented generation (RAG)

They use complex linear algebra

They process billions of parameters to generate accurate responses

They rely on human feedback and evaluation for all responses

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Why can RAG help lower hallucinations in AI models?

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It has guardrails for certain types of prompts

It searches a proprietary database to enhance the response

It disables the probability system

It relies only on human supervision

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Amon these choices, what is a core capability of Amazon Lex?

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It analyzes data for fraud detection

It allows for video analysis

It supports intents and slot filling to create conversational AI experiences

It automates the extraction of text from scanned documents

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What can you do with Amazon Comprehend?

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Analyze text to extract insights such as for sentiment, key phrases, and entities

Transcribe audio recordings into text

Convert text into humanlike speech

Translate text into multiple languages

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What is a major capability of Amazon Rekognition?

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The translation of spoken language into text

The use of optical character recognition

The generation of AI-powered chatbot responses

The ability to recognize faces, objects, and scenes in images and videos

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Among these options, which is a technique used in natural language processing (NLP) preprocessing?

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Automatically correcting all grammatical errors in a sentence

Translating text into another language before processing

Lemmatization, which reduces words to their root form

Applying deepfake detection to verify text authenticity

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Why is stopword removal used in natural language processing (NLP) preprocessing?

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Stopwords add important meaning to a text

It helps remove uncommon words to focus only on frequently used terms

Stopwords are removed to reduce text length to speed up processing

Removing stopwords like the and is helps improve efficiency without losing essential meaning

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Explanation

When is AI not always the best solution for a business use case?

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AI models do not work well in cloud-based environments

AI requiers human oversight at all times

AI can only be used for simple automation tasks

AI solutions can be complex and costly when a simpler approach may suffice

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What is the Compare Models feature in the Bedrock playground?

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It allows for using image and text responses

It provides for making longer responses for two different models

It combines responses from two models

It allows for evaluating the side-by-side responses from two different models

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Why would you fine-tune a model in Bedrock?

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To reduce compute costs

To improve latency

To improve response accuracy for domain specific scenarios

To use images

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What is a key advantage of using open source foundation models (FMs) in AWS Bedrock?

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They are always free to use

They support real-time inference by default

They offer transparency, customization, and community innovation

They provide the codebase and dataset

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What's a benefit of using a distilled model in Bedrock?

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It reduces compute requirements, making it suitable for edge devices

It increases the context window

It increases the creativity of the responses

It guarantees higher accuracy than larger models.

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What is a key benefit of multiagent collaboration in Bedrock?

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It relies on distilled models

It provides for 100% accuracy

It allows different agents to specialize in specific tasks, leading to better problem solving

It enables users to edit model weights and biases

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What is a key benefit of batch processing when using Bedrock?

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There is low latency

The context window is unlimited

There is higher accuracy

Lower costs

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Which technique improves the consistency and clarity of prompts when used repeatedly?

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Few-shot prompting

Zero-shot prompting

Chain-of-thought prompting

Prompt templates

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What is the purpose of chain-of-thought (CoT) prompting?

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To specify for the large language model (LLM) which examples to use

To limit the response of the large language model (LLM)

To allow for step-by-step reasoning for complex problems

To adjust the large language model (LLM) parameters

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Explanation

Which type of prompting involves providing several examples to help the model learn a pattern?

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Zero-shot prompting

Chain-of-thought (CoT) prompting

Few-shot prompting

Template prompting

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What is model poisoning?

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This allows a model to process personal health data

This is when biased or malicious data is injected into the training process

This when a model lacks proper licensing

This is where there are conflicting instructions in a single prompt

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What is exposure as it relates to security risks with foundation models (FMs)?

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The use of the model for consumer apps

The loss of GPU performance during training

The unintentional inclusion of sensitive data in training sets

The failure to generate output within token limits

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What is jailbreaking of a foundation model (FM)?

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Running a model without GPU acceleration

Resetting a model's API token for expanding the access

Deleting a model's training history

Tricking the model into bypassing safety and ethical restrictions

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Which of the following best describes AI governance?

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A set of marketing strategies for AI adoption

Policies and oversight structures to guide ethical AI development

Legal ownership of AI-generated content

Techniques for building large language models (LLMs)

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What does controllability in AI focus on?

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Reducing computational costs

Building faster AI systems

Aligning AI actions with human intent and oversight

Removing human involvement

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Which of the following is a business advantage of adopting responsible AI practices?

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Enhanced trust and improved brand reputation

Reduced need for data collection

Increased dependence on human intervention

Elimination of all algorithm errors

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What is the role of Amazon SageMaker Clarify in responsible AI?

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Detecting harmful URLs in user prompts

Managing compute costs during training

Identifying and explaining bias in data and models

Encrypting training data

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What is Amazon Augmented AI (A2I) primarily used for?

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Accelerating training of neural networks

Creating synthetic data from scratch

Managing GPUs in SageMaker

Integrating human reviews into AI workflows

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What is a key advantage of reinforcement learning from human feedback (RLHF)?

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It removes the need for model retraining

It ensures AI systems remain static

It helps models align outputs with human preferences and judgements

It eliminates the need for data labeling

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What is a compliance risk caused by AI systems developing emergent capabilities?

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They strictly follow preprogrammed features with no surprises

They may behave unpredictably in ways not planned by the original designers

They automatically file compliance reports as they change

They prevent the need for any human oversight

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What is a key characteristic of a regulated workload?

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It only applies to gaming or entertainment systems

It only focuses on making systems faster, not safer

It only requires encryption of stored data

It must follow specific compliance rules due to legal, industry, or safety concerns

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Why is data logging important in AI systems?

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it helps speed up model training by skipping error checks

It captures inputs, outputs, and system events, supporting debugging, monitoring, and transparency

It prevents the need for data residency compliance

It automatically guarantees 100% model accuracy

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Which AWS service provides a detailed history of resource configuration changes and relationships to support compliance auditing?

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AWS Config

AWS Trusted Advisor

Amazon Inspector

AWS Artifact

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In the Generative AI Security Scoping Matrix, which scope involves using publicly available generative AI tools without backend access or customization?

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Scope 3: Pretrained models

Scope 4: Fine-tuned models

Scope 5: Self-trained models

Scope 1: Consumer applications

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Which AWS service uses machine learning (ML) to detect and classify sensitive data like personally identifiable information (PII) or protected health information (PHI) across your environment?

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AWS Verified Permissions

AWS Shield Advanced

Amazon Macie

Amazon SageMaker Role Manager

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What is the purpose of a model card in the context of generative AI?

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To automatically retrain models with new data

To manage access controls across cloud infrastructure

To document the data sources, intended uses, risks, and limitations of a model

Top optimize compute resources for faster model deployment

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In a generative AI systems, who always controls user data, regardless of application scope?

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The application provider

The cloud service provider

The data annotation team

The customer or end user

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