Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 61:

  HOTSPOT

  An AI practitioner is determining the appropriate data type for various use cases.

  Select the correct data type from the following list for each use case. Select each data type one time.

  

  

  Explanation:

  Build a sentiment analysis model for social media posts -> Text data Train a self-driving car to recognize traffic signs -> Image data Optimize ad campaigns by using customer demographic data and purchase history -> Tabular data Forecast stock prices by using historical price data -> Time series data

  Sentiment analysis deals with text data from social media posts.

  Recognizing traffic signs requires processing image data.

  Optimizing ad campaigns uses structured (tabular) customer and purchase data.

  Stock price forecasting uses time series data, which captures trends over time.

• Question 62:

  A company needs to select a generative AI model to build an application. The application must provide responses to users in real time. Which model characteristic should the company consider to meet these requirements?

  A. Model complexity
  B. Innovation speed
  C. Inference speed
  D. Training time
  C. Inference speed

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  For real-time applications, the model's inference speed - ability to generate responses with low latency - is the critical characteristic to ensure users receive answers promptly.

• Question 63:

  A financial company is using AI systems to obtain customer credit scores as part of the loan application process. The company wants to expand to a new market in a different geographic area. The company must ensure that it can operate in that geographic area. Which compliance laws should the company review?

  A. Local health data protection laws
  B. Local payment card data protection laws
  C. Local education privacy laws
  D. Local algorithm accountability laws
  D. Local algorithm accountability laws

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  When deploying AI systems that affect individuals, such as credit scoring, companies must comply with local algorithm accountability laws. These laws regulate the fairness, transparency, and impact of automated decision-making, ensuring ethical use of AI in new geographic regions.

• Question 64:

  An AI practitioner must fine-tune an open source large language model (LLM) for text categorization. The dataset is already prepared. Which solution will meet these requirements with the LEAST operational effort?

  A. Create a custom model training job in PartyRock on Amazon Bedrock.
  B. Use Amazon SageMaker JumpStart to create a training job.
  C. Use a custom script to run an Amazon SageMaker AI model training job.
  D. Create a Jupyter notebook on an Amazon EC2 instance. Use the notebook to train the model.
  B. Use Amazon SageMaker JumpStart to create a training job.

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  SageMaker JumpStart provides prebuilt solutions and workflows for fine-tuning open source models with minimal setup. It reduces operational effort compared to custom scripts, EC2 notebooks, or PartyRock, making it the most efficient choice.

• Question 65:

  A company that streams media is selecting an Amazon Nova foundation model (FM) to process documents and images. The company is comparing Nova Micro and Nova Lite. The company wants to minimize costs.

  Which model characteristics should the company consider to meet these requirements?

  A. Nova Micro uses transformer-based architectures. Nova Lite does not use transformer-based architectures.
  B. Nova Micro supports only text data. Nova Lite is optimized for numerical data.
  C. Nova Micro supports only text. Nova Lite supports images, videos, and text.
  D. Nova Micro runs only on CPUs. Nova Lite runs only on GPUs.
  C. Nova Micro supports only text. Nova Lite supports images, videos, and text.

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  Explanation

  Nova Micro supports only text processing, while Nova Lite supports multimodal inputs including images, videos, and text. Selecting the model with broader multimodal support allows the company to choose the most cost-effective option that still meets document and image processing requirements.

• Question 66:

  A research group wants to test different generative AI models to create research papers. The research group has defined a prompt and needs a method to assess the models' output. The research group wants to use a team of scientists to perform the output assessments. Which solution will meet these requirements?

  A. Use automatic evaluation on Amazon Personalize.
  B. Use content moderation on Amazon Rekognition.
  C. Use model evaluation on Amazon Bedrock.
  D. Use sentiment analysis on Amazon Comprehend.
  C. Use model evaluation on Amazon Bedrock.

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  Amazon Bedrock provides model evaluation capabilities, allowing a team of scientists to manually assess and compare the outputs of different generative AI models based on defined prompts and criteria. This meets the requirement for human-in-the-loop model output evaluation.

• Question 67:

  Which statement correctly describes embeddings in generative AI?

  A. Embeddings represent data as high-dimensional vectors that capture semantic relationships.
  B. Embeddings is a technique that searches data to find the most helpful information to answer natural language questions.
  C. Embeddings reduce the hardware requirements of a model by using a less precise data type for the weights and activations.
  D. Embeddings provide the ability to store and retrieve data for generative AI applications.
  A. Embeddings represent data as high-dimensional vectors that capture semantic relationships.

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  Embeddings map inputs like words, sentences, or documents into continuous vector spaces where semantic similarity corresponds to geometric proximity, enabling models to reason about meaning and relationships mathematically.

• Question 68:

  A company needs to use Amazon SageMaker for model training and inference. The company must comply with regulatory requirements to run SageMaker jobs in an isolated environment without internet access. Which solution will meet these requirements?

  A. Run SageMaker training and inference by using SageMaker Experiments.
  B. Run SageMaker training and Inference by using network Isolation.
  C. Encrypt the data at rest by using encryption for SageMaker geospatial capabilities.
  D. Associate appropriate AWS Identity and Access Management (IAM) roles with the SageMaker jobs.
  B. Run SageMaker training and Inference by using network Isolation.

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  Network isolation in Amazon SageMaker allows you to run training and inference jobs in an environment that does not have access to the internet. This helps ensure that the data and the model do not inadvertently access external resources, meeting regulatory compliance requirements for isolated environments.

• Question 69:

  A company is using a large language model (LLM) to create a generative AI assistant. The company must choose an AI technique to ensure that the AI assistant generates the most factually correct responses. The company selects the Retrieval Augmented Generation (RAG) technique.

  Which limitation of LLMs is the company trying to reduce?

  A. Hallucinations
  B. Security
  C. Nondeterminism
  D. Interpretability
  A. Hallucinations

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  Explanation

  Retrieval Augmented Generation reduces hallucinations by grounding the model's responses in authoritative external data sources, which helps ensure that generated answers are based on factual and verifiable information rather than the model inventing incorrect details.

• Question 70:

  A company is introducing a new feature for its application. The feature will refine the style of output messages. The company will fine-tune a large language model (LLM) on Amazon Bedrock to implement the feature. Which type of data does the company need to meet these requirements?

  A. Samples of only input messages
  B. Samples of only output messages
  C. Samples of pairs of input and output messages
  D. Separate samples of input and output messages
  C. Samples of pairs of input and output messages

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  Fine-tuning a large language model to refine the style of output messages requires training data consisting of paired input and output messages. These pairs allow the model to learn the relationship between the input provided and the desired styled output, ensuring the fine-tuned model produces responses with the intended style.