Practice Free AIF-C01 Exam Online Questions
A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications.
Which factor will drive the inference costs?
- A . Number of tokens consumed
- B . Temperature value
- C . Amount of data used to train the LLM
- D . Total training time
A
Explanation:
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
Reference: Understanding Inference Costs on AWS: AWS documentation highlights that inference costs for generative models are largely based on the number of tokens processed.
A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.
Which AI learning strategy provides this self-improvement capability?
- A . Supervised learning with a manually curated dataset of good responses and bad responses
- B . Reinforcement learning with rewards for positive customer feedback
- C . Unsupervised learning to find clusters of similar customer inquiries
- D . Supervised learning with a continuously updated FAQ database
B
Explanation:
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
Reference: Reinforcement Learning on AWS: AWS provides reinforcement learning frameworks that can be used to train models to improve their performance based on feedback.
A company is building a chatbot to improve user experience. The company is using a large language model (LLM) from Amazon Bedrock for intent detection. The company wants to use few-shot learning to improve intent detection accuracy.
Which additional data does the company need to meet these requirements?
- A . Pairs of chatbot responses and correct user intents
- B . Pairs of user messages and correct chatbot responses
- C . Pairs of user messages and correct user intents
- D . Pairs of user intents and correct chatbot responses
C
Explanation:
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:
HOTSPOT
A company is using Amazon SageMaker to develop AI models.
Select the correct SageMaker feature or resource from the following list for each step in the AI model lifecycle workflow. Each
SageMaker feature or resource should be selected one time or not at all. (Select TWO.)
SageMaker Clarify
SageMaker Model Registry
SageMaker Serverless Inference
Explanation:
SageMaker Model Registry, SageMaker Serverless interference
This question requires selecting the appropriate Amazon SageMaker feature for two distinct steps in the AI model lifecycle.
Let’s break down each step and evaluate the options:
Step 1: Managing different versions of the model
The goal here is to identify a SageMaker feature that supports version control and management of machine learning models.
Let’s analyze the options:
SageMaker Clarify: This feature is used to detect bias in models and explain model predictions, helping with fairness and interpretability. It does not provide functionality for managing model versions.
SageMaker Model Registry: This is a centralized repository in Amazon SageMaker that allows users to catalog, manage, and track different versions of machine learning models. It supports model versioning, approval workflows, and deployment tracking, making it ideal for managing different versions of a model.
SageMaker Serverless Inference: This feature enables users to deploy models for inference without managing servers, automatically scaling based on demand. It is focused on inference (predictions), not on managing model versions.
Conclusion for Step 1: The SageMaker Model Registry is the correct choice for managing different versions of the model.
Exact Extract
Reference: According to the AWS SageMaker documentation, “The SageMaker Model Registry allows you to catalog models for production, manage model versions, associate metadata, and manage approval status for deployment.” (Source: AWS SageMaker Documentation – Model Registry, https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html). Step 2: Using the current model to make predictions
The goal here is to identify a SageMaker feature that facilitates making predictions (inference) with a deployed model. Let’s evaluate the options:
SageMaker Clarify: As mentioned, this feature focuses on bias detection and explainability, not on performing inference or making predictions.
SageMaker Model Registry: While the Model Registry helps manage and catalog models, it is not used directly for making predictions. It can store models, but the actual inference process requires a deployment mechanism.
SageMaker Serverless Inference: This feature allows users to deploy models for inference without managing infrastructure. It automatically scales based on traffic and is specifically designed for making predictions in a cost-efficient, serverless manner.
Conclusion for Step 2: SageMaker Serverless Inference is the correct choice for using the current model to make predictions.
Exact Extract
Reference: The AWS documentation states, “SageMaker Serverless Inference is a deployment option that allows you to deploy machine learning models for inference without configuring or managing servers. It automatically scales to handle inference requests, making it ideal for workloads with intermittent or unpredictable traffic.” (Source: AWS SageMaker Documentation – Serverless Inference, https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-inference.html).
Why Not Use the Same Feature Twice?
The question specifies that each SageMaker feature or resource should be selected one time or not at all. Since SageMaker Model Registry is used for version management and SageMaker Serverless Inference is used for predictions, each feature is selected exactly once. SageMaker Clarify is not applicable to either step, so it is not selected at all, fulfilling the question’s requirements.
Reference: AWS SageMaker Documentation: Model Registry
(https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html)
AWS SageMaker Documentation: Serverless Inference
(https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-inference.html)
AWS AI Practitioner Study Guide (conceptual alignment with SageMaker features for model lifecycle management and inference)
Let’s format this question according to the specified structure and provide a detailed, verified answer based on AWS AI Practitioner knowledge and official AWS documentation. The question focuses on selecting an AWS database service that supports storage and queries of embeddings as vectors, which is relevant to generative AI applications.
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis.
The company wants to classify the sentiment of text passages as positive or negative.
Which prompt engineering strategy meets these requirements?
- A . Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.
- B . Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
- C . Provide the new text passage to be classified without any additional context or examples.
- D . Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.
A
Explanation:
Providing examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified is the correct prompt engineering strategy for using a large language model (LLM) on Amazon Bedrock for sentiment analysis.
Example-Driven Prompts:
This strategy, known as few-shot learning, involves giving the model examples of input-output pairs (e.g., text passages with their sentiment labels) to help it understand the task context.
It allows the model to learn from these examples and apply the learned pattern to classify new text passages correctly.
Why Option A is Correct:
Guides the Model: Providing labeled examples teaches the model how to perform sentiment analysis effectively, increasing accuracy.
Contextual Relevance: Aligns the model’s responses to the specific task of classifying sentiment.
Why Other Options are Incorrect:
B. Detailed explanation of sentiment analysis: Unnecessary for the model’s operation; it requires examples, not explanations.
C. New text passage without context: Provides no guidance or learning context for the model.
D. Unrelated task examples: Would confuse the model and lead to inaccurate results.
A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost.
Which solution will meet these requirements?
- A . Customize the model by using fine-tuning.
- B . Decrease the number of tokens in the prompt.
- C . Increase the number of tokens in the prompt.
- D . Use Provisioned Throughput.
B
Explanation:
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 company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company’s products.
Which methodology should the company use to meet these requirements?
- A . Supervised learning
- B . Unsupervised learning
- C . Reinforcement learning
- D . Reinforcement learning from human feedback (RLHF)
B
Explanation:
Unsupervised learning is the correct methodology for classifying customers into tiers when the data is unlabeled, as it does not require predefined labels or outputs. Unsupervised Learning:
This type of machine learning is used when the data has no labels or pre-defined categories. The goal is to identify patterns, clusters, or associations within the data.
In this case, the company has petabytes of unlabeled customer data and needs to classify customers into different tiers. Unsupervised learning techniques like clustering (e.g., K-Means, Hierarchical Clustering) can group similar customers based on various attributes without any prior knowledge or labels.
Why Option B is Correct:
Handling Unlabeled Data: Unsupervised learning is specifically designed to work with unlabeled data, making it ideal for the company’s need to classify customer data.
Customer Segmentation: Techniques in unsupervised learning can be used to find natural groupings within customer data, such as identifying high-value vs. low-value customers or segmenting based on purchasing behavior.
Why Other Options are Incorrect:
A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.
Which Amazon Bedrock pricing model meets these requirements?
- A . On-Demand
- B . Model customization
- C . Provisioned Throughput
- D . Spot Instance
A
Explanation:
Amazon Bedrock offers an on-demand pricing model that provides flexibility without long-term commitments. This model allows companies to pay only for the resources they use, which is ideal for a limited budget and offers flexibility.
Option A (Correct): "On-Demand": This is the correct answer because on-demand pricing allows the company to use Amazon Bedrock without any long-term commitments and to manage costs according to their budget.
Option B: "Model customization" is a feature, not a pricing model.
Option C: "Provisioned Throughput" involves reserving capacity ahead of time, which might not offer the desired flexibility and could lead to higher costs if the capacity is not fully used.
Option D: "Spot Instance" is a pricing model for EC2 instances and does not apply to Amazon Bedrock.
AWS AI Practitioner
Reference: AWS Pricing Models for Flexibility: On-demand pricing is a key AWS model for services that require flexibility and no long-term commitment, ensuring cost-effectiveness for projects with variable usage patterns.
HOTSPOT
A company wants to create an application to summarize meetings by using meeting audio recordings.
Select and order the correct steps from the following list to create the application.
Each step should be selected one time or not at all. (Select and order THREE.)
• Convert meeting audio recordings to meeting text files by using Amazon Polly.
• Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
• Store meeting audio recordings in an Amazon S3 bucket.
• Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.
• Summarize meeting text files by using Amazon Bedrock.
• Summarize meeting text files by using Amazon Lex.
Explanation:
Step 1: Store meeting audio recordings in an Amazon S3 bucket.
Step 2: Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
Step 3: Summarize meeting text files by using Amazon Bedrock.
The company wants to create an application to summarize meeting audio recordings, which requires
a sequence of steps involving storage, speech-to-text conversion, and text summarization. Amazon S3 is the recommended storage service for audio files, Amazon Transcribe converts audio to text, and Amazon Bedrock provides generative AI capabilities for summarization. These three steps, in this order, create an efficient workflow for the application.
Exact Extract from AWS AI Documents:
From the Amazon Transcribe Developer Guide:
"Amazon Transcribe uses deep learning to convert audio files into text, supporting applications such as meeting transcription. Audio files can be stored in Amazon S3, and Transcribe can process them directly from an S3 bucket."
From the AWS Bedrock User Guide:
"Amazon Bedrock provides foundation models that can perform text summarization, enabling developers to build applications that generate concise summaries from text data, such as meeting
transcripts."
(Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe; AWS Bedrock User
Guide, Text Generation and Summarization)
Detailed
Step 1: Store meeting audio recordings in an Amazon S3 bucket. Amazon S3 is the standard storage service for audio files in AWS workflows, especially for integration with services like Amazon Transcribe. Storing the recordings in S3 allows Transcribe to access and process them efficiently. This is the first logical step.
Step 2: Convert meeting audio recordings to meeting text files by using Amazon Transcribe. Amazon Transcribe is designed for automatic speech recognition (ASR), converting audio files (stored in S3) into text. This step is necessary to transform the meeting recordings into a format that can be summarized.
Step 3: Summarize meeting text files by using Amazon Bedrock. Amazon Bedrock provides foundation models capable of generative AI tasks like text summarization. Once the audio is converted to text, Bedrock can summarize the meeting transcripts, completing the application’s requirements. Unused Options Analysis:
Convert meeting audio recordings to meeting text files by using Amazon Polly. Amazon Polly is a text-to-speech service, not for converting audio to text. This option is incorrect and not used.
Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume. Amazon EBS is for block storage, typically used for compute instances, not for storing files for processing by services like Transcribe. S3 is the better choice, so this option is not used.
Summarize meeting text files by using Amazon Lex. Amazon Lex is for building conversational
interfaces (chatbots), not for text summarization. Bedrock is the appropriate service for
summarization, so this option is not used.
Hotspot Selection Analysis:
The task requires selecting and ordering three steps from the list, with each step used exactly once or not at all. The selected steps―storing in S3, converting with Transcribe, and summarizing with Bedrock―form a complete and logical workflow for the application.
Reference: Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com/transcribe/latest/dg/what-is.html) AWS Bedrock User Guide: Text Generation and Summarization (https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) AWS AI Practitioner Learning Path: Module on Speech-to-Text and Generative AI Amazon S3 User Guide: Storing Data for Processing (https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html)
Which functionality does Amazon SageMaker Clarify provide?
- A . Integrates a Retrieval Augmented Generation (RAG) workflow
- B . Monitors the quality of ML models in production
- C . Documents critical details about ML models
- D . Identifies potential bias during data preparation
D
Explanation:
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data’s existing structure.
Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner
Reference: Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.