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  3. AIF-C01

Amazon AIF-C01 Exam (page: 3)
Amazon AWS Certified AI Practitioner
Updated on: 31-Mar-2026

Viewing Page 3 of 43
AIF-C01 PDF 422 Questions

A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products.

Which methodology should the company use to meet these requirements?

  1. Supervised learning
  2. Unsupervised learning
  3. Reinforcement learning
  4. Reinforcement learning from human feedback (RLHF)

Answer(s): B

Explanation:

Unsupervised learning is suitable for analyzing unlabeled data and grouping it into clusters or tiers, which aligns with the company's goal of classifying customers. The other methods require labeled data or are used for different types of problems.



An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.

Which type of FM should the AI practitioner use to power the search application?

  1. Multi-modal embedding model
  2. Text embedding model
  3. Multi-modal generation model
  4. Image generation model

Answer(s): A

Explanation:

A multi-modal embedding model can handle both text and image queries by embedding them into a shared space, enabling the search application to process and relate different data types. The other options are not suitable for handling both text and image inputs effectively.



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?

  1. Provide labeled data with the prompt field and the completion field.
  2. Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.
  3. Purchase Provisioned Throughput for Amazon Bedrock.
  4. Train the model on journals and textbooks.

Answer(s): A

Explanation:

Fine-tuning a foundation model involves training it with labeled data that contains both input prompts and corresponding expected completions to adjust the model's behavior to fit the company's needs. The other options are not directly related to the fine-tuning process using specific labeled data.



A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.

Which solution meets these requirements?

  1. Build a speech recognition system.
  2. Create a natural language processing (NLP) named entity recognition system.
  3. Develop an anomaly detection system.
  4. Create a fraud forecasting system.

Answer(s): C

Explanation:

An anomaly detection system can identify suspicious behavior, such as IP addresses that deviate from expected patterns, which helps in protecting the application from threats. The other options are not designed for detecting suspicious IP addresses.



Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?

  1. Integration with Amazon S3 for object storage
  2. Support for geospatial indexing and queries
  3. Scalable index management and nearest neighbor search capability
  4. Ability to perform real-time analysis on streaming data

Answer(s): C

Explanation:

The scalable index management and nearest neighbor search capability in Amazon OpenSearch Service enables companies to build vector database applications, which are crucial for tasks like similarity search in AI models. The other options do not specifically provide the vector search functionality.



Which option is a use case for generative AI models?

  1. Improving network security by using intrusion detection systems
  2. Creating photorealistic images from text descriptions for digital marketing
  3. Enhancing database performance by using optimized indexing
  4. Analyzing financial data to forecast stock market trends

Answer(s): B

Explanation:

Generative AI models are used to create new content, such as photorealistic images from text descriptions, which is useful for digital marketing. The other options involve tasks better suited for analytical or detection systems rather than generative models.



A company wants to build a generative AI application by using Amazon Bedrock and needs to choose a foundation model (FM). The company wants to know how much information can fit into one prompt.

Which consideration will inform the company's decision?

  1. Temperature
  2. Context window
  3. Batch size
  4. Model size

Answer(s): B

Explanation:

The context window determines how much information can fit into a single prompt. It specifies the number of tokens the foundation model can process at once, affecting the length of input that can be provided. The other options do not directly relate to prompt size.



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?

  1. Set a low limit on the number of tokens the FM can produce.
  2. Use batch inferencing to process detailed responses.
  3. Experiment and refine the prompt until the FM produces the desired responses.
  4. Define a higher number for the temperature parameter.

Answer(s): C

Explanation:

Experimenting and refining the prompt allows you to guide the FM to produce responses that align with the company's desired tone. This approach helps to shape the behavior of the chatbot. The other options do not directly ensure adherence to company tone.



Viewing Page 3 of 43



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