Amazon Web Services AIF-C01 AWS Certified AI Practitioner Exam(AI1-C01) Exam Practice Test

AWS CloudTrail is a service that enables governance, compliance, and operational and risk auditing of your AWS account. It tracks API calls and identifies unauthorized access attempts to AWS resources, including Amazon Bedrock.

AWS CloudTrail:

Provides detailed logs of all API calls made within an AWS account, including those to Amazon Bedrock.

Can identify unauthorized access attempts by logging and monitoring the API calls, which helps in setting appropriate IAM policies and roles.

Why Option B is Correct:

Monitoring and Security:CloudTrail logs all access requests and helps detect unauthorized access attempts.

Auditing and Compliance:The logs can be used to audit user activity and enforce security measures.

Why Other Options are Incorrect:

A. AWS Audit Manager:Used for automating audit preparation, not for tracking real-time unauthorized access attempts.

C. Amazon Fraud Detector:Designed to detect fraudulent online activities, not unauthorized access to AWS services.

D. AWS Trusted Advisor:Provides best practice recommendations for AWS resources, not access monitoring.

Thus,Bis the correct answer for identifying unauthorized users attempting to access Amazon Bedrock.

BERT-based models (Bidirectional Encoder Representations from Transformers) are suitable for tasks that involve understanding the context of words in a sentence and suggesting missing words. These models use bidirectional training, which considers the context from both directions (left and right of the missing word) to predict the appropriate word to fill in the gaps.

BERT-based Models:

BERT is a pre-trained transformer model designed for natural language understanding tasks, including text completion, where certain words are missing.

It excels at understanding context and relationships between words in a sentence, making it ideal for suggesting potential words to fill in missing text.

Why Option D is Correct:

Contextual Understanding:BERT uses its bidirectional training to understand the context around missing words, making it highly accurate in suggesting suitable replacements.

Text Completion Capability:BERT's architecture is explicitly designed for tasks like masked language modeling, where certain words in a text are masked (or missing), and the model predicts the missing words.

Why Other Options are Incorrect:

A. Topic modeling:Focuses on identifying topics in a text corpus, not on predicting missing words.

B. Clustering models:Group similar data points together, which is not suitable for predicting missing text.

C. Prescriptive ML models:Focus on providing recommendations based on data analysis, not on natural language processing tasks like filling in missing text.

A summarization chatbot is ideal for extracting key points from legal documents. Large language models (LLMs) can be used to summarize complex texts, such as legal documents, making them more accessible and understandable.

Option C (Correct): "Develop a summarization chatbot":This is the correct answer because a summarization chatbot uses LLMs to condense and extract key information from text, which is precisely the requirement for reading and summarizing legal documents.

Option A:"Build an automatic named entity recognition system" is incorrect because it focuses on identifying specific entities, not summarizing documents.

Option B:"Create a recommendation engine" is incorrect as it is used to suggest products or content, not summarize text.

Option D:"Develop a multi-language translation system" is incorrect because translation is unrelated to summarizing text.

AWS AI Practitioner References:

Using LLMs for Text Summarization on AWS:AWS supports developing summarization tools using its AI services, including Amazon Bedrock.

Moderation APIs, such as Amazon Rekognition’s Content Moderation API, can help filter and block inappropriate or unwanted images from being returned by a chatbot. These APIs are specifically designed to detect and manage undesirable content in images.

Option A (Correct): "Implement moderation APIs":This is the correct answer because moderation APIs are designed to identify and filter inappropriate content, ensuring the chatbot does not return unwanted images.

Option B:"Retrain the model with a general public dataset" is incorrect because retraining does not directly prevent inappropriate content from being returned.

Option C:"Perform model validation" is incorrect as it ensures model correctness, not content moderation.

Option D:"Automate user feedback integration" is incorrect because user feedback does not prevent inappropriate images in real-time.

AWS AI Practitioner References:

AWS Content Moderation Services:AWS provides moderation APIs for filtering unwanted content from applications.

A confusion matrix is the correct metric for evaluating the performance of a classification model, such as the deep learning model built to classify types of materials in images.

Confusion Matrix:

It is a table used to describe the performance of a classification model by comparing the actual and predicted classifications.

Provides detailed insights into the model’s performance, including true positives, true negatives, false positives, and false negatives.

Why Option A is Correct:

Performance Measurement:Helps measure various performance metrics like accuracy, precision, recall, and F1-score, which are critical for evaluating a classification model.

Comprehensive Evaluation:Allows for a thorough analysis of where the model is making errors and the types of errors being made.

Why Other Options are Incorrect:

B. Correlation matrix:Used to identify relationships between variables, not for evaluating classification performance.

C. R2 score:Used for regression models, not classification.

D. Mean squared error (MSE):Also a metric for regression, measuring the average of the squares of the errors.

To comply with the company's security policy, which restricts each team to access data for only their own customers, creating an Amazon Bedrock custom service role for each team is the correct solution.

Custom Service Role Per Team:

A custom service role for each team ensures that the access control is granular, allowing only specific teams to access their own customer data in Amazon S3.

This setup aligns with the principle of least privilege, ensuring teams can only interact with data they are authorized to access.

Why Option A is Correct:

Access Control:Allows precise access permissions for each team's data.

Security Compliance:Directly meets the company's security policy requirements by ensuring data segregation.

Why Other Options are Incorrect:

B. Custom service role with customer name specification:This approach is impractical as it relies on manual input, which is prone to errors and does not inherently enforce data access controls.

C. Redacting personal data and updating S3 bucket policy:Redaction does not solve the requirement for team-specific access, and updating bucket policies is less granular than creating roles.

D. One Bedrock role with full S3 access and IAM roles for teams:This setup does not meet the least privilege principle, as having a single role with full access is contrary to the company's security policy.

Thus,Ais the correct answer to meet the company's security requirements.

An anomaly detection system is suitable for identifying unusual patterns or behaviors, such as suspicious IP addresses, which might indicate a potential threat.

Anomaly Detection:

Anomaly detection uses machine learning algorithms to identify deviations from normal behavior, such as unexpected traffic from a suspicious IP address.

This is a common approach for identifying potential threats or malicious activity in cybersecurity applications.

Why Option C is Correct:

Detects Suspicious Behavior:An anomaly detection system can monitor and detect IP addresses that exhibit unusual or suspicious patterns.

Real-time Monitoring:Provides continuous analysis of network traffic to identify potential security threats.

Why Other Options are Incorrect:

A. Speech recognition system:Is unrelated to detecting suspicious IP addresses.

B. NLP named entity recognition:Focuses on identifying entities in text, not IP address analysis.

D. Fraud forecasting system:Generally used for predicting fraud, not directly applicable to identifying suspicious IPs.

Thus,Cis the correct answer for detecting suspicious IP addresses.

To manage the flow of data from Amazon S3 to SageMaker Studio notebooks securely, using a VPC with an S3 endpoint is the best solution.

Amazon SageMaker and S3 Integration:

Configuring SageMaker to use a Virtual Private Cloud (VPC) with an S3 endpoint allows the data flow between Amazon S3 and SageMaker Studio notebooks to occur over a private network.

This setup ensures that traffic between SageMaker and S3 does not traverse the public internet, enhancing security and performance.

Why Option C is Correct:

Secure Data Transfer:Ensures secure, private connectivity between SageMaker and S3, reducing exposure to potential security risks.

Direct Access to S3:Using an S3 endpoint in a VPC allows direct access to data in S3 without leaving the AWS network.

Why Other Options are Incorrect:

A. Amazon Inspector:Focuses on identifying security vulnerabilities, not managing data flow.

B. Amazon Macie:Monitors for sensitive data but does not manage data flow between S3 and SageMaker.

D. S3 Glacier Deep Archive:Is a storage class for archiving data, not for managing active data flow.

Amazon QuickSight is a fully managed business intelligence (BI) service that allows users to create and publish interactive dashboards that include visualizations like graphs, charts, and tables. "Amazon Q" is the natural language query feature within Amazon QuickSight. It enables users to ask questions about their data in natural language and receive visual responses such as graphs.

Option C (Correct): "Amazon Q in Amazon QuickSight":This is the correct answer because Amazon QuickSight Q is specifically designed to allow users to explore their data through natural language queries, and it can automatically generate graphs to display sales data and other metrics. This makes it an ideal choice for the company to automate the generation of graphs showing total sales for its top-selling products across various retail locations.

Option A, B, and D:These options are incorrect:

A. Amazon Q in Amazon EC2:Amazon EC2 is a compute service that provides virtual servers, but it is not directly related to generating graphs or providing natural language querying features.

B. Amazon Q Developer:This is not an existing AWS service or feature.

D. Amazon Q in AWS Chatbot:AWS Chatbot is a service that integrates with Amazon Chime and Slack for monitoring and managing AWS resources, but it is not used for generating graphs based on sales data.

AWS AI Practitioner References:

Amazon QuickSight Qis designed to provide insights from data by using natural language queries, making it a powerful tool for generating automated graphs and visualizations directly from queried data.

Business Intelligence (BI) on AWS:AWS services such as Amazon QuickSight provide business intelligence capabilities, including automated reporting and visualization features, which are ideal for companies seeking to visualize data like sales trends over time.

Benchmark datasets are pre-validated datasets specifically designed to evaluate machine learning models for bias, fairness, and potential discrimination. These datasets are the most efficient tool for assessing an LLM’s performance against known standards with minimal administrative effort.

Option D (Correct): "Benchmark datasets":This is the correct answer because using standardized benchmark datasets allows the company to evaluate model outputs for bias with minimal administrative overhead.

Option A:"User-generated content" is incorrect because it is unstructured and would require significant effort to analyze for bias.

Option B:"Moderation logs" is incorrect because they represent historical data and do not provide a standardized basis for evaluating bias.

Option C:"Content moderation guidelines" is incorrect because they provide qualitative criteria rather than a quantitative basis for evaluation.

AWS AI Practitioner References:

Evaluating AI Models for Bias on AWS:AWS supports using benchmark datasets to assess model fairness and detect potential bias efficiently.

Amazon Comprehend is the correct service to analyze customer support interactions and identify frequently asked questions and insights.

Amazon Comprehend:

A natural language processing (NLP) service that uses machine learning to uncover insights and relationships in text.

Capable of extracting key phrases, detecting entities, analyzing sentiment, and identifying topics from text data, making it ideal for analyzing customer support interactions.

Why Option B is Correct:

Text Analysis Capabilities:Can process large volumes of text to identify common topics, phrases, and sentiment, providing valuable insights.

Suitable for Customer Support Analysis:Specifically designed to understand the content and meaning of text, which is key for identifying frequently asked questions.

Why Other Options are Incorrect:

A. Amazon Lex:Used for building conversational interfaces, not for text analysis.

C. Amazon Transcribe:Converts speech to text but does not perform text analysis.

D. Amazon Translate:Used for translating text between languages, not for analyzing content.

Reinforcement learning allows a model to learn and improve over time based on feedback from its environment. In this case, the chatbot can improve its responses by being rewarded for positive customer feedback, which aligns well with the goal of self-improvement based on past interactions and new information.

Option B (Correct): "Reinforcement learning with rewards for positive customer feedback":This is the correct answer as reinforcement learning enables the chatbot to learn from feedback and adapt its behavior accordingly, providing self-improvement capabilities.

Option A:"Supervised learning with a manually curated dataset" is incorrect because it does not support continuous learning from new interactions.

Option C:"Unsupervised learning to find clusters of similar customer inquiries" is incorrect because unsupervised learning does not provide a mechanism for improving responses based on feedback.

Option D:"Supervised learning with a continuously updated FAQ database" is incorrect because it still relies on manually curated data rather than self-improvement from feedback.

AWS AI Practitioner References:

Reinforcement Learning on AWS:AWS provides reinforcement learning frameworks that can be used to train models to improve their performance based on feedback.

Amazon Bedrock Agents provide the capability to automate repetitive tasks and orchestrate complex workflows using generative AI models. This is particularly beneficial for customer support inquiries, where quick and efficient processing is crucial.

Option B (Correct): "Automation of repetitive tasks and orchestration of complex workflows":This is the correct answer because Bedrock Agents can automate common customer service tasks and streamline complex processes, improving response times and efficiency.

Option A:"Generation of custom foundation models (FMs) to predict customer needs" is incorrect as Bedrock agents do not create custom models.

Option C:"Automatically calling multiple foundation models (FMs) and consolidating the results" is incorrect because Bedrock agents focus on task automation rather than combining model outputs.

Option D:"Selecting the foundation model (FM) based on predefined criteria and metrics" is incorrect as Bedrock agents are not designed for selecting models.

AWS AI Practitioner References:

Amazon Bedrock Documentation:AWS explains that Bedrock Agents automate tasks and manage complex workflows, making them ideal for customer support automation.

To build an AI model responsibly and minimize bias, it is essential to ensure fairness and transparency throughout the model development and deployment process. This involves detecting and mitigating data imbalances and thoroughly evaluating the model's behavior to understand its impact on different groups.

Option A (Correct): "Detect imbalances or disparities in the data":This is correct because identifying and addressing data imbalances or disparities is a critical step in reducing bias. AWS provides tools like Amazon SageMaker Clarify to detect bias during data preprocessing and model training.

Option C (Correct): "Evaluate the model's behavior so that the company can provide transparency to stakeholders":This is correct because evaluating the model's behavior for fairness and accuracy is key to ensuring that stakeholders understand how the model makes decisions. Transparency is a crucial aspect of responsible AI.

Option B:"Ensure that the model runs frequently" is incorrect because the frequency of model runs does not address bias.

Option D:"Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate" is incorrect because ROUGE is a metric for evaluating the quality of text summarization models, not for minimizing bias.

Option E:"Ensure that the model's inference time is within the accepted limits" is incorrect as it relates to performance, not bias reduction.

AWS AI Practitioner References:

Amazon SageMaker Clarify:AWS offers tools such as SageMaker Clarify for detecting bias in datasets and models, and for understanding model behavior to ensure fairness and transparency.

Responsible AI Practices:AWS promotes responsible AI by advocating for fairness, transparency, and inclusivity in model development and deployment.

Adjusting the prompt is the correct solution to align the LLM outputs with the company's expectations for short, specific language responses.

Adjust the Prompt:

Modifying the prompt can guide the LLM to produce outputs that are shorter and tailored to the desired language.

A well-crafted prompt can provide specific instructions to the model, such as "Answer in a short sentence in Spanish."

Why Option A is Correct:

Control Over Output:Adjusting the prompt allows for direct control over the style, length, and language of the LLM outputs.

Flexibility:Prompt engineering is a flexible approach to refining the model’s behavior without modifying the model itself.

Why Other Options are Incorrect:

B. Choose an LLM of a different size:The model size does not directly impact the response length or language.

C. Increase the temperature:Increases randomness in responses but does not ensure brevity or specific language.

D. Increase the Top K value:Affects diversity in model output but does not align directly with response length or language specificity.

Amazon SageMaker Canvas is a visual, no-code machine learning interface that allows users to build machine learning models without having any coding experience or knowledge of machine learning algorithms. It enables users to analyze internal and external data, and make predictions using a guided interface.

Option D (Correct): "Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas":This is the correct answer because SageMaker Canvas is designed for users without coding experience, providing a visual interface to build predictive models with ease.

Option A:"Store the data in Amazon S3 and use SageMaker built-in algorithms" is incorrect because it requires coding knowledge to interact with SageMaker's built-in algorithms.

Option B:"Import the data into Amazon SageMaker Data Wrangler" is incorrect. Data Wrangler is primarily for data preparation and not directly focused on creating ML models without coding.

Option C:"Use Amazon Personalize Trending-Now recipe" is incorrect as Amazon Personalize is for building recommendation systems, not for general demand forecasting.

AWS AI Practitioner References:

Amazon SageMaker Canvas Overview:AWS documentation emphasizes Canvas as a no-code solution for building machine learning models, suitable for business analysts and users with no coding experience.

Creating a prompt template that teaches the LLM to detect attack patterns is the most effective way to reduce the risk of the model being manipulated through prompt engineering.

Prompt Templates for Security:

A well-designed prompt template can guide the LLM to recognize and respond appropriately to potential manipulation attempts.

This strategy helps prevent the model from performing undesirable actions or exposing sensitive information by embedding security awareness directly into the prompts.

Why Option A is Correct:

Teaches Model Security Awareness:Equips the LLM to handle potentially harmful inputs by recognizing suspicious patterns.

Reduces Manipulation Risk:Helps mitigate risks associated with prompt engineering attacks by proactively preparing the LLM.

Why Other Options are Incorrect:

B. Increase the temperature parameter:This increases randomness in responses, potentially making the LLM more unpredictable and less secure.

C. Avoid LLMs not listed in SageMaker:Does not directly address the risk of prompt manipulation.

D. Decrease the number of input tokens:Does not mitigate risks related to prompt manipulation.

Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.

Token Reduction Strategy:

By decreasing the number of tokens (words or characters) in each prompt, the company reduces the computational load and, therefore, the cost associated with invoking the model.

Since the model is performing well with few-shot prompting, reducing token usage without sacrificing performance can lower monthly costs.

Why Option B is Correct:

Cost Efficiency:Directly reduces the number of tokens processed, lowering costs without requiring additional adjustments.

Maintaining Performance:If the model is already performing well, a reduction in tokens should not significantly impact its performance.

Why Other Options are Incorrect:

A. Fine-tuning:Can be costly and time-consuming and is not needed if the current model is already performing well.

C. Increase the number of tokens:Would increase costs, not lower them.

D. Use Provisioned Throughput:Is unrelated to token costs and applies more to read/write capacity in databases.

The Amazon EC2 Trn series (Trainium) instances are designed for high-performance, cost-effective machine learning training while being energy-efficient. AWS Trainium-powered instances are optimized for deep learning models and have been developed to minimize environmental impact by maximizing energy efficiency.

Option D (Correct): "Amazon EC2 Trn series":This is the correct answer because the Trn series is purpose-built for training deep learning models with lower energy consumption, which aligns with the company's concern about environmental effects.

Option A:"Amazon EC2 C series" is incorrect because it is intended for compute-intensive tasks but not specifically optimized for ML training with environmental considerations.

Option B:"Amazon EC2 G series" (Graphics Processing Unit instances) is optimized for graphics-intensive applications but does not focus on minimizing environmental impact for training.

Option C:"Amazon EC2 P series" is designed for ML training but does not offer the same level of energy efficiency as the Trn series.

AWS AI Practitioner References:

AWS Trainium Overview:AWS promotes Trainium instances as their most energy-efficient and cost-effective solution for ML model training.

To achieve the lowest latency possible for inference on edge devices, deploying optimized small language models (SLMs) is the most effective solution. SLMs require fewer resources and have faster inference times, making them ideal for deployment on edge devices where processing power and memory are limited.

Option A (Correct): "Deploy optimized small language models (SLMs) on edge devices":This is the correct answer because SLMs provide fast inference with low latency, which is crucial for edge deployments.

Option B:"Deploy optimized large language models (LLMs) on edge devices" is incorrect because LLMs are resource-intensive and may not perform well on edge devices due to their size and computational demands.

Option C:"Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices" is incorrect because it introduces network latency due to the need for communication with a centralized server.

Option D:"Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices" is incorrect for the same reason, with even greater latency due to the larger model size.

AWS AI Practitioner References:

Optimizing AI Models for Edge Devices on AWS:AWS recommends using small, optimized models for edge deployments to ensure minimal latency and efficient performance.

Amazon Bedrock provides an option to enable invocation logging to capture and store the input and output data of the models used. This is essential for monitoring and auditing purposes, particularly when handling customer data.

Option B (Correct): "Enable invocation logging in Amazon Bedrock":This is the correct answer as it directly enables the logging of all model invocations, ensuring transparency and traceability.

Option A:"Configure AWS CloudTrail" is incorrect because CloudTrail logs API calls but does not provide specific logging for model inputs and outputs.

Option C:"Configure AWS Audit Manager" is incorrect as Audit Manager is used for compliance reporting, not specific invocation logging for AI models.

Option D:"Configure model invocation logging in Amazon EventBridge" is incorrect as EventBridge is for event-driven architectures, not specifically designed for logging AI model inputs and outputs.

AWS AI Practitioner References:

Amazon Bedrock Logging Capabilities:AWS emphasizes using built-in logging features in Bedrock to maintain data integrity and transparency in model operations.

Amazon Transcribe is the correct solution for converting audio from customer calls into text, allowing the company to analyze and extract key information from the conversations.

Amazon Transcribe:

It is a fully managed automatic speech recognition (ASR) service that converts speech into text, making it easier to perform text-based analysis on audio data.

After transcribing the audio, further analysis can be performed using other AWS services like Amazon Comprehend to extract insights such as sentiment, key phrases, or entities.

Why Option B is Correct:

Conversion to Text:Transcribing audio recordings is the first step in gaining insights from spoken conversations, allowing for further processing.

Enables Further Analysis:Once the audio is transcribed into text, other tools and services can be used to analyze the content more deeply.

Why Other Options are Incorrect:

A. Amazon Lex:Is used for building conversational interfaces, not for transcribing or analyzing audio from customer calls.

C. Amazon SageMaker Model Monitor:Monitors ML models for bias and data drift, not for audio analysis.

D. Amazon Comprehend:Can analyze text but cannot transcribe audio; it would be used after transcription is completed.

Few-shot learning involves providing a model with a few examples (shots) to learn from. For improving intent detection accuracy in a chatbot using a large language model (LLM), the data should consist of pairs of user messages and their corresponding correct intents.

Few-shot Learning for Intent Detection:

Few-shot learning aims to enable the model to learn from a small number of examples. For intent detection, the model needs to understand the relationship between user messages and the intended action or meaning.

Providing examples of user messages and the correct user intents allows the model to learn patterns in the phrasing or language that corresponds to each intent.

Why Option C is Correct:

User Messages and Intents:These examples directly teach the model how to map a user’s input to the appropriate intent, which is the goal of intent detection in chatbots.

Improves Accuracy:By using few-shot learning with these examples, the model can generalize better from limited data, improving intent detection.

Why Other Options are Incorrect:

A. Pairs of chatbot responses and correct user intents:Incorrect because it does not focus on user input but rather on outputs.

B. Pairs of user messages and correct chatbot responses:This would be useful for response generation, not intent detection.

D. Pairs of user intents and correct chatbot responses:Again, this is not aligned with detecting intents but with generating responses.

A multi-modal embedding model is the correct type of foundation model (FM) for powering a search application that handles queries containing both text and images.

Multi-Modal Embedding Model:

Can process and integrate different types of data (e.g., text and images) into a common representation space, enabling a unified search capability.

Suitable for applications where queries or content involve multiple data modalities.

Why Option A is Correct:

Handles Multiple Modalities:Supports both text and image data, aligning with the application's requirement.

Improves Search Relevance:Allows for more accurate and relevant search results across different types of input data.

Why Other Options are Incorrect:

B. Text embedding model:Only handles text data, not images.

C. Multi-modal generation model:Focuses on generating outputs rather than embedding for search tasks.

D. Image generation model:Only handles image data, not suitable for text queries.

Amazon Bedrock is a service that provides foundational models for building generative AI applications. When creating an application for children, it is crucial to ensure that the generated content is appropriate for the target audience. "Guardrails" in Amazon Bedrock provide mechanisms to control the outputs and topics of generated content to align with desired safety standards and appropriateness levels.

Option C (Correct): "Guardrails for Amazon Bedrock":This is the correct answer because guardrails are specifically designed to help users enforce content moderation, filtering, and safety checks on the outputs generated by models in Amazon Bedrock. For a children’s application, guardrails ensure that all content generated is suitable and appropriate for the intended audience.

Option A:"Amazon Rekognition" is incorrect. Amazon Rekognition is an image and video analysis service that can detect inappropriate content in images or videos, but it does not handle text or story generation.

Option B:"Amazon Bedrock playgrounds" is incorrect because playgrounds are environments for experimenting and testing model outputs, but they do not inherently provide safeguards to ensure content appropriateness for specific audiences, such as children.

Option D:"Agents for Amazon Bedrock" is incorrect. Agents in Amazon Bedrock facilitate building AI applications with more interactive capabilities, but they do not provide specific guardrails for ensuring content appropriateness for children.

AWS AI Practitioner References:

Guardrails in Amazon Bedrock:Designed to help implement controls that ensure generated content is safe and suitable for specific use cases or audiences, such as children, by moderating and filtering inappropriate or undesired content.

Building Safe AI Applications:AWS provides guidance on implementing ethical AI practices, including using guardrails to protect against generating inappropriate or biased content.

In generative AI models, such as those built on Amazon Bedrock, inference costs are driven by the number of tokens processed. A token can be as short as one character or as long as one word, and the more tokens consumed during the inference process, the higher the cost.

Option A (Correct): "Number of tokens consumed":This is the correct answer because the inference cost is directly related to the number of tokens processed by the model.

Option B:"Temperature value" is incorrect as it affects the randomness of the model's output but not the cost directly.

Option C:"Amount of data used to train the LLM" is incorrect because training data size affects training costs, not inference costs.

Option D:"Total training time" is incorrect because it relates to the cost of training the model, not the cost of inference.

AWS AI Practitioner References:

Understanding Inference Costs on AWS:AWS documentation highlights that inference costs for generative models are largely based on the number of tokens processed.