Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 191:

  A company is developing an editorial assistant application that uses generative AI. During the pilot phase, usage is low and application performance is not a concern. The company cannot predict application usage after the application is fully deployed and wants to minimize application costs. Which solution will meet these requirements?

  A. Use GPU-powered Amazon EC2 instances.
  B. Use Amazon Bedrock with Provisioned Throughput.
  C. Use Amazon Bedrock with On-Demand Throughput.
  D. Use Amazon SageMaker JumpStart.
  C. Use Amazon Bedrock with On-Demand Throughput.

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  On-Demand Throughput in Bedrock charges only for the inference calls you actually make, with no upfront capacity commitment. This lets you start with minimal pilot usage and elastically handle unpredictable future load while keeping costs as low as possible.

• Question 192:

  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
  B. Amazon SageMaker Ground Truth Plus

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  Amazon SageMaker Ground Truth Plus provides managed data labeling services, allowing you to label datasets using human feedback without developing labeling applications or managing a labeling workforce.

• Question 193:

  A research company implemented a chatbot by using a foundation model (FM) from Amazon Bedrock. The chatbot searches for answers to questions from a large database of research papers.

  After multiple prompt engineering attempts, the company notices that the FM is performing poorly because of the complex scientific terms in the research papers.

  How can the company improve the performance of the chatbot?

  A. Use few-shot prompting to define how the FM can answer the questions.
  B. Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.
  C. Change the FM inference parameters.
  D. Clean the research paper data to remove complex scientific terms.
  B. Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.

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  Domain adaptation fine-tuning involves training a foundation model (FM) further using a specific dataset that includes domain-specific terminology and content, such as scientific terms in research papers. This process allows the model to better understand and handle complex terminology, improving its performance on specialized tasks.

  Option B (Correct): "Use domain adaptation fine-tuning to adapt the FM to complex scientific terms":This is the correct answer because fine- tuning the model on domain-specific data helps it learn and adapt to the specific language and terms used in the research papers, resulting in better performance.

  Option A:"Use few-shot prompting to define how the FM can answer the questions" is incorrect because while few-shot prompting can help in certain scenarios, it is less effective than fine-tuning for handling complex domain-specific terms.

  Option C:"Change the FM inference parameters" is incorrect because adjusting inference parameters will not resolve the issue of the model's lack of understanding of complex scientific terminology.

  Option D:"Clean the research paper data to remove complex scientific terms" is incorrect because removing the complex terms would result in the loss of important information and context, which is not a viable solution.

  References:

  Domain Adaptation in Amazon Bedrock:AWS recommends fine-tuning models with domain-specific data to improve their performance on specialized tasks involving unique terminology.

• Question 194:

  A large retail bank wants to develop an ML system to help the risk management team decide on loan allocations for different demographics. What must the bank do to develop an unbiased ML model?

  A. Reduce the size of the training dataset.
  B. Ensure that the ML model predictions are consistent with historical results.
  C. Create a different ML model for each demographic group.
  D. Measure class imbalance on the training dataset. Adapt the training process accordingly.
  D. Measure class imbalance on the training dataset. Adapt the training process accordingly.

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  In machine learning, class imbalance occurs when certain classes are underrepresented in the training dataset, leading to biased model predictions. To develop an unbiased model, it's crucial to assess the class distribution and adjust the training process to address any imbalances. This can be achieved through techniques such as oversampling the minority class, undersampling the majority class, or applying class weights to ensure the model treats all classes equitably.

• Question 195:

  A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images.

  Which solution will meet these requirements?

  A. Implement moderation APIs.
  B. Retrain the model with a general public dataset.
  C. Perform model validation.
  D. Automate user feedback integration.
  A. Implement moderation APIs.

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  Moderation APIs, such as Amazon Rekognition's Content Moderation API, can help filter and block inappropriate or unwanted images from being returned by a chatbot. These APIs are specifically designed to detect and manage undesirable content in images.

  Option A (Correct): "Implement moderation APIs":This is the correct answer because moderation APIs are designed to identify and filter inappropriate content, ensuring the chatbot does not return unwanted images.

  Option B:"Retrain the model with a general public dataset" is incorrect because retraining does not directly prevent inappropriate content from being returned.

  Option C:"Perform model validation" is incorrect as it ensures model correctness, not content moderation.

  Option D:"Automate user feedback integration" is incorrect because user feedback does not prevent inappropriate images in real-time.

  References:

  AWS Content Moderation Services:AWS provides moderation APIs for filtering unwanted content from applications.

• Question 196:

  HOTSPOT

  A company wants to build a new ML solution. The company already has data. The company needs to understand the ML lifecycle before building the solution.

  Select and order the steps from the following list to correctly describe the ML lifecycle. Select each step one time.

  

  

• Question 197:

  Which technique involves training AI models on labeled datasets to adapt the models to specific industry terminology and requirements?

  A. Data augmentation
  B. Fine-tuning
  C. Model quantization
  D. Continuous pre-training
  B. Fine-tuning

• Question 198:

  A company uses foundation models (FMs) to automate daily tasks. An AI practitioner at the company is creating system instructions that include context that is relevant to the tasks. The AI practitioner wants to save and reuse the instructions in daily interactions with FMs in Amazon Bedrock.

  Which Amazon Bedrock solution will meet these requirements?

  A. Knowledge Bases
  B. Guardrails
  C. Playgrounds
  D. Prompt management
  D. Prompt management

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  Explanation

  Prompt management in Amazon Bedrock lets you create, save, version, and reuse prompts, including system instructions and task context, across repeated interactions.

• Question 199:

  A company runs a website for users to make travel reservations. The company wants an AI solution to help create consistent branding for hotels on the website.

  The AI solution needs to generate hotel descriptions for the website in a consistent writing style.

  Which AWS service will meet these requirements?

  A. Amazon Comprehend
  B. Amazon Personalize
  C. Amazon Rekognition
  D. Amazon Bedrock
  D. Amazon Bedrock

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  Amazon Bedrock provides access to foundation models for generative AI tasks such as creating text in a consistent style. It is the correct service to generate hotel descriptions with uniform branding for the website.

• Question 200:

  A real estate company is developing an ML model to predict house prices by using sales and marketing data. The company wants to use feature engineering to build a model that makes accurate predictions.

  Which approach will meet these requirements?

  A. Understand patterns by providing data visualization.
  B. Tune the model's hyperparameters.
  C. Create or select relevant features for model training.
  D. Collect data from multiple sources.
  C. Create or select relevant features for model training.

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  Explanation

  Feature engineering focuses on creating, transforming, and selecting the most relevant input variables for model training, which directly improves the model's ability to learn meaningful patterns and make accurate house price predictions.