Amazon Amazon Certifications AIF-C01 Questions & Answers

• Question 41:

  A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.

  What should the company do to meet these requirements?

  A. Evaluate the models by using built-in prompt datasets.

  B. Evaluate the models by using a human workforce and custom prompt datasets.

  C. Use public model leaderboards to identify the model.

  D. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.

  Correct Answer: B

  To determine which model generates responses in a style that the company's employees prefer, the best approach is to use a human workforce to evaluate the models with custom prompt datasets. This method allows for subjective

  evaluation based on the specific stylistic preferences of the company's employees, which cannot be effectively assessed through automated methods or pre-built datasets. Option B (Correct): "Evaluate the models by using a human workforce

  and custom prompt datasets":This is the correct answer as it directly involves human judgment to evaluate the style and quality of the responses, aligning with employee preferences.

  Option A:"Evaluate the models by using built-in prompt datasets" is incorrect because built-in datasets may not capture the company's specific stylistic requirements.

  Option C:"Use public model leaderboards to identify the model" is incorrect as leaderboards typically measure model performance on standard benchmarks, not on stylistic preferences.

  Option D:"Use the model InvocationLatency runtime metrics in Amazon CloudWatch" is incorrect because latency metrics do not provide any information about the style of the model's responses.

  AWS AI Practitioner

  References:

  Model Evaluation Techniques on AWS:AWS suggests using human evaluators to assess qualitative aspects of model outputs, such as style and tone, to ensure alignment with organizational preferences

• Question 42:

  A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.

  Which solution will meet these requirements?

  A. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.

  B. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.

  C. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.

  D. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.

  Correct Answer: D

  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.

• Question 43:

  Which metric measures the runtime efficiency of operating AI models?

  A. Customer satisfaction score (CSAT)

  B. Training time for each epoch

  C. Average response time

  D. Number of training instances

  Correct Answer: C

  The average response time is the correct metric for measuring the runtime efficiency of operating AI models.

• Question 44:

  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.

  Correct Answer: A

  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.

• Question 45:

  A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data.

  Which strategy will successfully fine-tune the model?

  A. Provide labeled data with the prompt field and the completion field.

  B. Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.

  C. Purchase Provisioned Throughput for Amazon Bedrock.

  D. Train the model on journals and textbooks.

  Correct Answer: A

  Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.

• Question 46:

  Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?

  A. Amazon Personalize

  B. Amazon SageMaker JumpStart

  C. PartyRock, an Amazon Bedrock Playground

  D. Amazon SageMaker endpoints

  Correct Answer: B

  Amazon SageMaker JumpStart is the correct service for quickly deploying and consuming a foundation model (FM) within a team's VPC.

• Question 47:

  A company has thousands of customer support interactions per day and wants to analyze these interactions to identify frequently asked questions and develop insights.

  Which AWS service can the company use to meet this requirement?

  A. Amazon Lex

  B. Amazon Comprehend

  C. Amazon Transcribe

  D. Amazon Translate

  Correct Answer: B

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

• Question 48:

  An AI practitioner has a database of animal photos. The AI practitioner wants to automatically identify and categorize the animals in the photos without manual human effort.

  Which strategy meets these requirements?

  A. Object detection

  B. Anomaly detection

  C. Named entity recognition

  D. Inpainting

  Correct Answer: A

  Object detection is the correct strategy for automatically identifying and categorizing animals in photos.

• Question 49:

  A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention. The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that

  adhere to company tone.

  Which solution meets these requirements?

  A. Set a low limit on the number of tokens the FM can produce.

  B. Use batch inferencing to process detailed responses.

  C. Experiment and refine the prompt until the FM produces the desired responses.

  D. Define a higher number for the temperature parameter.

  Correct Answer: C

  Experimenting and refining the prompt is the best approach to ensure that the chatbot using a foundation model (FM) produces responses that adhere to the company's tone.

• Question 50:

  A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.

  What can Amazon Q Developer do to help the company meet these requirements?

  A. Create software snippets, reference tracking, and open-source license tracking.

  B. Run an application without provisioning or managing servers.

  C. Enable voice commands for coding and providing natural language search.

  D. Convert audio files to text documents by using ML models.

  Correct Answer: A

  Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating

  parts of the software development process.

  Option A (Correct): "Create software snippets, reference tracking, and open- source license tracking":This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.

  Option B:"Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer. Option C:"Enable voice commands for coding and providing natural language

  search" is incorrect because this is not a function of Amazon Q Developer. Option D:"Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.

  AWS AI Practitioner References:

  Amazon Q Developer Features:AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.