Practice Free AIF-C01 Exam Online Questions
Question #61
A company needs to build its own large language model (LLM) based on only the company’s private data. The company is concerned about the environmental effect of the training process.
Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?
- A . Amazon EC2 C series
- B . Amazon EC2 G series
- C . Amazon EC2 P series
- D . Amazon EC2 Trn series
Correct Answer: D
D
Explanation:
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
Reference: AWS Trainium Overview: AWS promotes Trainium instances as their most energy-efficient and cost-effective solution for ML model training.
D
Explanation:
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
Reference: AWS Trainium Overview: AWS promotes Trainium instances as their most energy-efficient and cost-effective solution for ML model training.
Question #62
An AI practitioner is developing a new ML model. After training the model, the AI practitioner evaluates the accuracy of the model’s predictions. The model’s accuracy is low when the model uses both the training dataset and the test dataset.
Which scenario is the MOST likely cause of this problem?
- A . Overfitting
- B . Hallucination
- C . Underfitting
- D . Cross-validation
Correct Answer: C
C
Explanation:
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the training data. AWS documentation explains that an underfit model performs poorly on both training and test datasets, which directly matches the scenario described.
In this case, the model shows low accuracy during training and evaluation, indicating that it has not learned sufficient relationships from the data. AWS identifies common causes of underfitting as insufficient model complexity, inadequate feature representation, overly aggressive regularization, or insufficient training time.
Underfitting is different from overfitting. Overfitting occurs when a model performs well on training
data but poorly on test data, which is not the situation here. Hallucination applies to generative AI outputs, not supervised ML model accuracy. Cross-validation is a model evaluation technique, not a cause of poor performance.
AWS emphasizes the importance of diagnosing underfitting early in the model development lifecycle. Remedies include increasing model complexity, adding relevant features, reducing regularization, or selecting a more expressive algorithm. These steps allow the model to better learn from the data and improve accuracy across both training and test sets.
AWS machine learning best practices clearly associate low performance on both datasets with underfitting, making this the most likely cause of the problem described.
C
Explanation:
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the training data. AWS documentation explains that an underfit model performs poorly on both training and test datasets, which directly matches the scenario described.
In this case, the model shows low accuracy during training and evaluation, indicating that it has not learned sufficient relationships from the data. AWS identifies common causes of underfitting as insufficient model complexity, inadequate feature representation, overly aggressive regularization, or insufficient training time.
Underfitting is different from overfitting. Overfitting occurs when a model performs well on training
data but poorly on test data, which is not the situation here. Hallucination applies to generative AI outputs, not supervised ML model accuracy. Cross-validation is a model evaluation technique, not a cause of poor performance.
AWS emphasizes the importance of diagnosing underfitting early in the model development lifecycle. Remedies include increasing model complexity, adding relevant features, reducing regularization, or selecting a more expressive algorithm. These steps allow the model to better learn from the data and improve accuracy across both training and test sets.
AWS machine learning best practices clearly associate low performance on both datasets with underfitting, making this the most likely cause of the problem described.
Question #63
A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer calls.
Which solution meets these requirements?
- A . Build a conversational chatbot by using Amazon Lex.
- B . Transcribe call recordings by using Amazon Transcribe.
- C . Extract information from call recordings by using Amazon SageMaker Model Monitor.
- D . Create classification labels by using Amazon Comprehend.
Correct Answer: B
B
Explanation:
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:
B
Explanation:
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:
Question #64
A company is using supervised learning to train an AI model on a small labeled dataset that is specific to a target task.
Which step of the foundation model (FM) lifecycle does this describe?
- A . Fine-tuning
- B . Data selection
- C . Pre-training
- D . Evaluation
Correct Answer: A
A
Explanation:
Fine-tuning involves training an already pre-trained FM on a smaller, labeled dataset for task specialization.
Data selection is about curating training data.
Pre-training is the initial training phase on massive datasets. Evaluation happens after training, not during.
Reference: AWS Documentation C Fine-tuning in Amazon Bedrock
A
Explanation:
Fine-tuning involves training an already pre-trained FM on a smaller, labeled dataset for task specialization.
Data selection is about curating training data.
Pre-training is the initial training phase on massive datasets. Evaluation happens after training, not during.
Reference: AWS Documentation C Fine-tuning in Amazon Bedrock
Question #65
A company wants to label training datasets by using human feedback to fine-tune a foundation model (FM). The company does not want to develop labeling applications or manage a labeling workforce.
Which AWS service or feature meets these requirements?
- A . Amazon SageMaker Data Wrangler
- B . Amazon SageMaker Ground Truth Plus
- C . Amazon Transcribe
- D . Amazon Macie
Correct Answer: B
B
Explanation:
Amazon SageMaker Ground Truth Plus provides a fully managed data labeling service where AWS manages the workforce, tools, and processes.
Data Wrangler is for data preparation and transformation.
Transcribe is for speech-to-text.
Macie is for sensitive data discovery, not labeling.
Reference: AWS Documentation C SageMaker Ground Truth Plus
B
Explanation:
Amazon SageMaker Ground Truth Plus provides a fully managed data labeling service where AWS manages the workforce, tools, and processes.
Data Wrangler is for data preparation and transformation.
Transcribe is for speech-to-text.
Macie is for sensitive data discovery, not labeling.
Reference: AWS Documentation C SageMaker Ground Truth Plus
Question #66
A company wants to build a customer-facing generative AI application. The application must block or mask sensitive information. The application must also detect hallucinations.
Which solution will meet these requirements with the LEAST operational overhead?
- A . Use AWS Lambda functions to build a policy evaluator.
- B . Select a foundation model (FM) that includes policies that remove harmful content by default.
- C . Use Amazon Bedrock Guardrails to implement safeguards for the application based on use cases.
- D . Host a custom-built policy evaluator on Amazon EC2 instances.
Correct Answer: C
C
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS recommends using managed, purpose-built services to enforce safety, compliance, and responsible AI controls in generative AI applications in order to minimize operational complexity and maintenance effort.
Amazon Bedrock Guardrails are specifically designed to help customers:
Block or mask sensitive information, such as personally identifiable information (PII)
Detect and reduce hallucinations by enforcing grounding and response constraints
Apply content filters, topic restrictions, and safety policies consistently across generative AI applications
Configure safeguards without building or managing custom infrastructure
Because Guardrails are fully managed and integrated directly with Amazon Bedrock, they require minimal setup, no custom code for policy enforcement, and no infrastructure management, resulting in the least operational overhead.
Why the other options are less suitable:
C
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS recommends using managed, purpose-built services to enforce safety, compliance, and responsible AI controls in generative AI applications in order to minimize operational complexity and maintenance effort.
Amazon Bedrock Guardrails are specifically designed to help customers:
Block or mask sensitive information, such as personally identifiable information (PII)
Detect and reduce hallucinations by enforcing grounding and response constraints
Apply content filters, topic restrictions, and safety policies consistently across generative AI applications
Configure safeguards without building or managing custom infrastructure
Because Guardrails are fully managed and integrated directly with Amazon Bedrock, they require minimal setup, no custom code for policy enforcement, and no infrastructure management, resulting in the least operational overhead.
Why the other options are less suitable:
Question #67
A company wants to create a chatbot to answer employee questions about company policies.
Company policies are updated frequently. The chatbot must reflect the changes in near real time.
The company wants to choose a large language model (LLM).
- A . Fine-tune an LLM on the company policy text by using Amazon SageMaker.
- B . Select a foundation model (FM) from Amazon Bedrock to build an application.
- C . Create a Retrieval Augmented Generation (RAG) workflow by using Amazon Bedrock Knowledge Bases.
- D . Use Amazon Q Business to build a custom Q App.
Correct Answer: C
C
Explanation:
The correct answer is C because Retrieval-Augmented Generation (RAG) allows a large language model to provide responses based on up-to-date content from external data sources without the need to fine-tune the model.
According to the AWS Bedrock Developer Guide:
"Amazon Bedrock Knowledge Bases enables developers to augment foundation models (FMs) with company-specific data that is updated in real time or near real time. By separating retrieval from the model itself, RAG-based approaches avoid the need for frequent retraining or fine-tuning."
This means a company can use a knowledge base with Amazon Bedrock to dynamically fetch the latest company policy information and feed it to the LLM in the prompt. This approach is ideal for use
cases where the content (like policies) changes frequently, and latency for updates must be minimal.
Explanation of other options:
C
Explanation:
The correct answer is C because Retrieval-Augmented Generation (RAG) allows a large language model to provide responses based on up-to-date content from external data sources without the need to fine-tune the model.
According to the AWS Bedrock Developer Guide:
"Amazon Bedrock Knowledge Bases enables developers to augment foundation models (FMs) with company-specific data that is updated in real time or near real time. By separating retrieval from the model itself, RAG-based approaches avoid the need for frequent retraining or fine-tuning."
This means a company can use a knowledge base with Amazon Bedrock to dynamically fetch the latest company policy information and feed it to the LLM in the prompt. This approach is ideal for use
cases where the content (like policies) changes frequently, and latency for updates must be minimal.
Explanation of other options:
Question #68
Which strategy evaluates the accuracy of a foundation model (FM) that is used in image classification tasks?
- A . Calculate the total cost of resources used by the model.
- B . Measure the model’s accuracy against a predefined benchmark dataset.
- C . Count the number of layers in the neural network.
- D . Assess the color accuracy of images processed by the model.
Correct Answer: B
B
Explanation:
Measuring the model’s accuracy against a predefined benchmark dataset is the correct strategy to evaluate the accuracy of a foundation model (FM) used in image classification tasks.
Model Accuracy Evaluation:
In image classification, the accuracy of a model is typically evaluated by comparing the predicted labels with the true labels in a benchmark dataset that is representative of the real-world data the model will encounter.
This approach provides a quantifiable measure of how well the model performs on known data and is a standard practice in machine learning.
Why Option B is Correct:
Benchmarking Accuracy: Using a predefined dataset allows for consistent and reliable evaluation of model performance.
Standard Practice: It is a widely accepted method for assessing the effectiveness of image classification models.
Why Other Options are Incorrect:
B
Explanation:
Measuring the model’s accuracy against a predefined benchmark dataset is the correct strategy to evaluate the accuracy of a foundation model (FM) used in image classification tasks.
Model Accuracy Evaluation:
In image classification, the accuracy of a model is typically evaluated by comparing the predicted labels with the true labels in a benchmark dataset that is representative of the real-world data the model will encounter.
This approach provides a quantifiable measure of how well the model performs on known data and is a standard practice in machine learning.
Why Option B is Correct:
Benchmarking Accuracy: Using a predefined dataset allows for consistent and reliable evaluation of model performance.
Standard Practice: It is a widely accepted method for assessing the effectiveness of image classification models.
Why Other Options are Incorrect:
Question #69
A company needs to monitor the performance of its ML systems by using a highly scalable AWS service.
Which AWS service meets these requirements?
- A . Amazon CloudWatch
- B . AWS CloudTrail
- C . AWS Trusted Advisor
- D . AWS Config
Correct Answer: A
A
Explanation:
Amazon CloudWatch is designed for real-time monitoring of applications and infrastructure. It supports metrics and logs for ML model performance and resource utilization.
According to the AWS Certified AI Practitioner Study Guide:
“Amazon CloudWatch is a monitoring service that provides data and actionable insights to monitor your ML workloads and applications in real time, ensuring performance and scalability.”
A
Explanation:
Amazon CloudWatch is designed for real-time monitoring of applications and infrastructure. It supports metrics and logs for ML model performance and resource utilization.
According to the AWS Certified AI Practitioner Study Guide:
“Amazon CloudWatch is a monitoring service that provides data and actionable insights to monitor your ML workloads and applications in real time, ensuring performance and scalability.”
Question #70
An ecommerce company wants to improve search engine recommendations by customizing the results for each user of the company’s ecommerce platform.
Which AWS service meets these requirements?
- A . Amazon Personalize
- B . Amazon Kendra
- C . Amazon Rekognition
- D . Amazon Transcribe
Correct Answer: A
A
Explanation:
The ecommerce company wants to improve search engine recommendations by customizing results for each user. Amazon Personalize is a machine learning service that enables personalized recommendations, tailoring search results or product suggestions based on individual user behavior and preferences, making it the best fit for this requirement.
Exact Extract from AWS AI Documents:
From the Amazon Personalize Developer Guide:
"Amazon Personalize enables developers to build applications with personalized recommendations, such as customized search results or product suggestions, by analyzing user behavior and preferences to deliver tailored experiences."
(Source: Amazon Personalize Developer Guide, Introduction to Amazon Personalize)
Detailed
Option A: Amazon Personalize This is the correct answer. Amazon Personalize specializes in creating personalized recommendations, ideal for customizing search results for each user on an ecommerce platform.
Option B: Amazon Kendra Amazon Kendra is an intelligent search service for enterprise data, focusing on retrieving relevant documents or answers, not on personalizing search results for individual users.
Option C: Amazon Rekognition Amazon Rekognition is for image and video analysis, such as object detection or facial recognition, and is unrelated to search engine recommendations.
Option D: Amazon Transcribe Amazon Transcribe converts speech to text, which is not relevant for improving search engine recommendations.
Reference: Amazon Personalize Developer Guide: Introduction to Amazon Personalize (https://docs.aws.amazon.com/personalize/latest/dg/what-is-personalize.html)
AWS AI Practitioner Learning Path: Module on Recommendation Systems
AWS Documentation: Personalization with Amazon Personalize (https://aws.amazon.com/personalize/)
A
Explanation:
The ecommerce company wants to improve search engine recommendations by customizing results for each user. Amazon Personalize is a machine learning service that enables personalized recommendations, tailoring search results or product suggestions based on individual user behavior and preferences, making it the best fit for this requirement.
Exact Extract from AWS AI Documents:
From the Amazon Personalize Developer Guide:
"Amazon Personalize enables developers to build applications with personalized recommendations, such as customized search results or product suggestions, by analyzing user behavior and preferences to deliver tailored experiences."
(Source: Amazon Personalize Developer Guide, Introduction to Amazon Personalize)
Detailed
Option A: Amazon Personalize This is the correct answer. Amazon Personalize specializes in creating personalized recommendations, ideal for customizing search results for each user on an ecommerce platform.
Option B: Amazon Kendra Amazon Kendra is an intelligent search service for enterprise data, focusing on retrieving relevant documents or answers, not on personalizing search results for individual users.
Option C: Amazon Rekognition Amazon Rekognition is for image and video analysis, such as object detection or facial recognition, and is unrelated to search engine recommendations.
Option D: Amazon Transcribe Amazon Transcribe converts speech to text, which is not relevant for improving search engine recommendations.
Reference: Amazon Personalize Developer Guide: Introduction to Amazon Personalize (https://docs.aws.amazon.com/personalize/latest/dg/what-is-personalize.html)
AWS AI Practitioner Learning Path: Module on Recommendation Systems
AWS Documentation: Personalization with Amazon Personalize (https://aws.amazon.com/personalize/)