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Free Amazon AIF-C01 Exam Braindumps (page: 5)

Page 5 of 48
PDF with 233 Questions

A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer calls.
Which solution meets these requirements?

  1. Build a conversational chatbot by using Amazon Lex.
  2. Transcribe call recordings by using Amazon Transcribe.
  3. Extract information from call recordings by using Amazon SageMaker Model Monitor.
  4. Create classification labels by using Amazon Comprehend.

Answer(s): B

Explanation:

Amazon Transcribe converts audio recordings into text, which allows for further analysis and extraction of key information from customer conversations. The other options do not directly handle audio transcription or extraction of information from audio.



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.



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