Amazon Amazon Certifications AIF-C01 Questions & Answers

• Question 51:

  A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.

  Which AWS service meets these requirements?

  A. Amazon S3

  B. Amazon Elastic Block Store (Amazon EBS)

  C. Amazon Elastic File System (Amazon EFS)

  D. AWS Snowcone

  Correct Answer: A

  Amazon S3 is the optimal choice for storing and uploading datasets used for machine learning model validation and training. It offers scalable, durable, and secure storage, making it ideal for holding datasets required by Amazon Bedrock for

  validation purposes. Option A (Correct): "Amazon S3":This is the correct answer because Amazon S3 is widely used for storing large datasets that are accessed by machine learning models, including those in Amazon Bedrock.

  Option B:"Amazon Elastic Block Store (Amazon EBS)" is incorrect because EBS is a block storage service for use with Amazon EC2, not for directly storing datasets for Amazon Bedrock.

  Option C:"Amazon Elastic File System (Amazon EFS)" is incorrect as it is primarily used for file storage with shared access by multiple instances. Option D:"AWS Snowcone" is incorrect because it is a physical device for offline data transfer,

  not suitable for directly providing data to Amazon Bedrock.

  AWS AI Practitioner

  References:

  Storing and Managing Datasets on AWS for Machine Learning:AWS recommends using S3 for storing and managing datasets required for ML model training and validation.

• Question 52:

  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

  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.

• Question 53:

  A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt.

  Which adjustment to an inference parameter should the company make to meet these requirements?

  A. Decrease the temperature value

  B. Increase the temperature value

  C. Decrease the length of output tokens

  D. Increase the maximum generation length

  Correct Answer: A

  The temperature parameter in a large language model (LLM) controls the randomness of the model's output. A lower temperature value makes the output more deterministic and consistent, meaning that the model is less likely to produce

  different results for the same input prompt.

  Option A (Correct): "Decrease the temperature value":This is the correct answer because lowering the temperature reduces the randomness of the responses, leading to more consistent outputs for the same input. Option B:"Increase the

  temperature value" is incorrect because it would make the output more random and less consistent.

  Option C:"Decrease the length of output tokens" is incorrect as it does not directly affect the consistency of the responses.

  Option D:"Increase the maximum generation length" is incorrect because this adjustment affects the output length, not the consistency of the model's responses.

  AWS AI Practitioner References:

  Understanding Temperature in Generative AI Models:AWS documentation explains that adjusting the temperature parameter affects the model's output randomness, with lower values providing more consistent outputs.

• Question 54:

  A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts.

  An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders.

  What should the AI practitioner include in the report to meet the transparency and explainability requirements?

  A. Code for model training

  B. Partial dependence plots (PDPs)

  C. Sample data for training

  D. Model convergence tables

  Correct Answer: B

  Partial dependence plots (PDPs) are visual tools used to show the relationship between a feature (or a set of features) in the data and the predicted outcome of a machine learning model. They are highly effective for providing transparency

  and explainability of the model's behavior to stakeholders by illustrating how different input variables impact the model's predictions.

  Option B (Correct): "Partial dependence plots (PDPs)":This is the correct answer because PDPs help to interpret how the model's predictions change with varying values of input features, providing stakeholders with a clearer understanding of

  the model's decision-making process.

  Option A:"Code for model training" is incorrect because providing the raw code for model training may not offer transparency or explainability to non-technical stakeholders.

  Option C:"Sample data for training" is incorrect as sample data alone does not explain how the model works or its decision-making process. Option D:"Model convergence tables" is incorrect. While convergence tables can show the training

  process, they do not provide insights into how input features affect the model's predictions.

  AWS AI Practitioner

  References:

  Explainability in AWS Machine Learning:AWS provides various tools for model explainability, such as Amazon SageMaker Clarify, which includes PDPs to help explain the impact of different features on the model's predictions.

• Question 55:

  Which option is a use case for generative AI models?

  A. Improving network security by using intrusion detection systems

  B. Creating photorealistic images from text descriptions for digital marketing

  C. Enhancing database performance by using optimized indexing

  D. Analyzing financial data to forecast stock market trends

  Correct Answer: B

  Generative AI models are used to create new content based on existing data. One common use case is generating photorealistic images from text descriptions, which is particularly useful in digital marketing, where visual content is key to

  engaging potential customers.

  Option B (Correct): "Creating photorealistic images from text descriptions for digital marketing":This is the correct answer because generative AI models, like those offered by Amazon Bedrock, can create images based on text descriptions,

  making them highly valuable for generating marketing materials. Option A:"Improving network security by using intrusion detection systems" is incorrect because this is a use case for traditional machine learning models, not generative AI.

  Option C:"Enhancing database performance by using optimized indexing" is incorrect as it is unrelated to generative AI.

  Option D:"Analyzing financial data to forecast stock market trends" is incorrect because it typically involves predictive modeling rather than generative AI.

  AWS AI Practitioner

  References:

  Use Cases for Generative AI Models on AWS:AWS highlights the use of generative AI for creative content generation, including image creation, text generation, and more, which is suited for digital marketing applications.

• Question 56:

  A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.

  Which solution meets these requirements?

  A. Optimize the model's architecture and hyperparameters to improve the model's overall performance.

  B. Increase the model's complexity by adding more layers to the model's architecture.

  C. Create effective prompts that provide clear instructions and context to guide the model's generation.

  D. Select a large, diverse dataset to pre-train a new generative model.

  Correct Answer: C

  Creating effective prompts is the best solution to ensure that the content generated by a pre-trained generative AI model aligns with the company's brand voice and messaging requirements.

• Question 57:

  A company has built a solution by using generative AI. The solution uses large language models (LLMs) to translate training manuals from English into other languages. The company wants to evaluate the accuracy of the solution by examining the text generated for the manuals.

  Which model evaluation strategy meets these requirements?

  A. Bilingual Evaluation Understudy (BLEU)

  B. Root mean squared error (RMSE)

  C. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)

  D. F1 score

  Correct Answer: A

  BLEU (Bilingual Evaluation Understudy) is a metric used to evaluate the accuracy of machine-generated translations by comparing them against reference translations. It is commonly used for translation tasks to measure how close the

  generated output is to professional human translations.

  Option A (Correct): "Bilingual Evaluation Understudy (BLEU)":This is the correct answer because BLEU is specifically designed to evaluate the quality of translations, making it suitable for the company's use case. Option B:"Root mean

  squared error (RMSE)" is incorrect because RMSE is used for regression tasks to measure prediction errors, not translation quality. Option C:"Recall-Oriented Understudy for Gisting Evaluation (ROUGE)" is incorrect as it is used to evaluate

  text summarization, not translation. Option D:"F1 score" is incorrect because it is typically used for classification tasks, not for evaluating translation accuracy.

  AWS AI Practitioner

  References:

  Model Evaluation Metrics on AWS:AWS supports various metrics like BLEU for specific use cases, such as evaluating machine translation models.

• Question 58:

  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)

  Correct Answer: A

  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.

• Question 59:

  A company has a database of petabytes of unstructured data from internal sources. The company wants to transform this data into a structured format so that its data scientists can perform machine learning (ML) tasks.

  Which service will meet these requirements?

  A. Amazon Lex

  B. Amazon Rekognition

  C. Amazon Kinesis Data Streams

  D. AWS Glue

  Correct Answer: D

  AWS Glue is the correct service for transforming petabytes of unstructured data into a structured format suitable for machine learning tasks.

• Question 60:

  A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible.

  Which solution will meet these requirements?

  A. Deploy optimized small language models (SLMs) on edge devices.

  B. Deploy optimized large language models (LLMs) on edge devices.

  C. Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.

  D. Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.

  Correct Answer: A

  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.