Practice Free AIF-C01 Exam Online Questions
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:
An AI practitioner has built a deep learning model to classify the types of materials in images. The AI practitioner now wants to measure the model performance.
Which metric will help the AI practitioner evaluate the performance of the model?
- A . Confusion matrix
- B . Correlation matrix
- C . R2 score
- D . Mean squared error (MSE)
A
Explanation:
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.
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?
- A . Data pre-processing
- B . Feature engineering
- C . Exploratory data analysis
- D . Hyperparameter tuning
C
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.
Which option is an example of unsupervised learning?
- A . Clustering data points into groups based on their similarity
- B . Training a model to recognize images of animals
- C . Predicting the price of a house based on the house’s features
- D . Generating human-like text based on a given prompt
A
Explanation:
Unsupervised learning involves discovering hidden patterns without labeled data.
Example: clustering.
Image recognition (B) is supervised learning.
House price prediction (C) is regression (supervised).
A media company wants to analyze viewer behavior and demographics to recommend personalized content. The company wants to deploy a customized ML model in its production environment. The company also wants to observe if the model quality drifts over time.
Which AWS service or feature meets these requirements?
- A . Amazon Rekognition
- B . Amazon SageMaker Clarify
- C . Amazon Comprehend
- D . Amazon SageMaker Model Monitor
D
Explanation:
The requirement is to deploy a customized machine learning (ML) model and monitor its quality for potential drift over time in a production environment. Let’s evaluate each option:
A social media company wants to use a large language model (LLM) to summarize messages. The company has chosen a few LLMs that are available on Amazon SageMaker JumpStart. The company wants to compare the generated output toxicity of these models.
Which strategy gives the company the ability to evaluate the LLMs with the LEAST operational overhead?
- A . Crowd-sourced evaluation
- B . Automatic model evaluation
- C . Model evaluation with human workers
- D . Reinforcement learning from human feedback (RLHF)
B
Explanation:
The least operational overhead comes from automated tools that can scan and evaluate LLM outputs for toxicity. AWS and SageMaker JumpStart support integrations with automatic evaluation tools and APIs (such as Amazon Comprehend or third-party toxicity classifiers).
B is correct: Automated evaluation provides quick, scalable, and repeatable analysis, requiring minimal human intervention.
A and C require manual effort, increasing operational overhead.
D (RLHF) is resource-intensive and not designed for rapid, automated model comparison.
"Automated evaluation can quickly assess generated text for specific attributes like toxicity, sentiment, or compliance using pre-trained classifiers, reducing human involvement and operational complexity."
(Reference: AWS SageMaker JumpStart Evaluation, AWS AI Practitioner Guide)
A customer service team is developing an application to analyze customer feedback and automatically classify the feedback into different categories. The categories include product quality, customer service, and delivery experience.
Which AI concept does this scenario present?
- A . Computer vision
- B . Natural language processing (NLP)
- C . Recommendation systems
- D . Fraud detection
B
Explanation:
The scenario involves analyzing customer feedback and automatically classifying it into categories such as product quality, customer service, and delivery experience. This task requires processing and understanding textual data, which is a core application of natural language processing (NLP). NLP encompasses techniques for analyzing, interpreting, and generating human language, including tasks like text classification, sentiment analysis, and topic modeling, all of which are relevant to this use case.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Natural Language Processing (NLP) enables machines to understand and process human language. Common NLP tasks include text classification, sentiment analysis, named entity recognition, and topic modeling. Services like Amazon Comprehend can be used to classify text into predefined categories based on content."
(Source: AWS AI Practitioner Learning Path, Module on AI and ML Concepts)
Detailed
Option A: Computer visionComputer vision involves processing and analyzing visual data, such as images or videos. Since the scenario deals with textual customer feedback, computer vision is not applicable.
Option B: Natural language processing (NLP)This is the correct answer. The task of classifying customer feedback into categories requires understanding and processing text, which is an NLP task. AWS services like Amazon Comprehend are specifically designed for such text classification tasks.
Option C: Recommendation systemsRecommendation systems suggest items or content based on user preferences or behavior. The scenario does not involve recommending products or services but rather classifying feedback, so this option is incorrect.
Option D: Fraud detectionFraud detection involves identifying anomalous or fraudulent activities, typically in financial or transactional data. The scenario focuses on text classification, not anomaly detection, making this option irrelevant.
Reference: AWS AI Practitioner Learning Path: Module on AI and ML Concepts
Amazon Comprehend Developer Guide: Text Classification (https://docs.aws.amazon.com/comprehend/latest/dg/how-classification.html)
AWS Documentation: Introduction to NLP (https://aws.amazon.com/what-is/natural-language-processing/)
An ecommerce company is deploying a chatbot. The chatbot will give users the ability to ask questions about the company’s products and receive details on users’ orders. The company must implement safeguards for the chatbot to filter harmful content from the input prompts and chatbot responses.
Which AWS feature or resource meets these requirements?
- A . Amazon Bedrock Guardrails
- B . Amazon Bedrock Agents
- C . Amazon Bedrock inference APIs
- D . Amazon Bedrock custom models
A
Explanation:
The ecommerce company is deploying a chatbot that needs safeguards to filter harmful content from input prompts and responses. Amazon Bedrock Guardrails provide mechanisms to ensure responsible AI usage by filtering harmful content, such as hate speech, violence, or misinformation, making it the appropriate feature for this requirement.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Amazon Bedrock Guardrails enable developers to implement safeguards for generative AI applications, such as chatbots, by filtering harmful content in input prompts and model responses. Guardrails include content filters, word filters, and denied topics to ensure safe and responsible outputs."
(Source: AWS Bedrock User Guide, Guardrails for Responsible AI)
Detailed
Option A: Amazon Bedrock Guardrails This is the correct answer. Amazon Bedrock Guardrails are specifically designed to filter harmful content from chatbot inputs and responses, ensuring safe interactions for users.
Option B: Amazon Bedrock Agents Amazon Bedrock Agents are used to automate tasks and integrate with external tools, not to filter harmful content. This option does not meet the requirement.
Option C: Amazon Bedrock inference APIs Amazon Bedrock inference APIs allow users to invoke foundation models for generating responses, but they do not provide built-in content filtering mechanisms.
Option D: Amazon Bedrock custom models Custom models on Amazon Bedrock allow users to fine-tune models, but they do not inherently include safeguards for filtering harmful content unless explicitly implemented.
Reference: AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html)
AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety
Amazon Bedrock Developer Guide: Building Safe AI Applications (https://aws.amazon.com/bedrock/)
Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?
- A . Embeddings
- B . Tokens
- C . Models
- D . Binaries
A
Explanation:
Embeddings are numerical representations of objects (such as words, sentences, or documents) that capture the objects’ semantic meanings in a form that AI and NLP models can easily understand. These representations help models improve their understanding of textual information by representing concepts in a continuous vector space.
Option A (Correct): "Embeddings": This is the correct term, as embeddings provide a way for models to learn relationships between different objects in their input space, improving their understanding and processing capabilities.
Option B: "Tokens" are pieces of text used in processing, but they do not capture semantic meanings like embeddings do.
Option C: "Models" are the algorithms that use embeddings and other inputs, not the representations themselves.
Option D: "Binaries" refer to data represented in binary form, which is unrelated to the concept of embeddings.
AWS AI Practitioner
Reference: Understanding Embeddings in AI and NLP: AWS provides resources and tools, like Amazon SageMaker, that utilize embeddings to represent data in formats suitable for machine learning models.
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)