FANTASTIC AIF-C01 NEW BRAINDUMPS BOOK & LEADER IN QUALIFICATION EXAMS & PASS-SURE AIF-C01: AWS CERTIFIED AI PRACTITIONER

Fantastic AIF-C01 New Braindumps Book & Leader in Qualification Exams & Pass-Sure AIF-C01: AWS Certified AI Practitioner

Fantastic AIF-C01 New Braindumps Book & Leader in Qualification Exams & Pass-Sure AIF-C01: AWS Certified AI Practitioner

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Amazon AWS Certified AI Practitioner Sample Questions (Q64-Q69):

NEW QUESTION # 64
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 Elastic File System (Amazon EFS)
  • B. Amazon Elastic Block Store (Amazon EBS)
  • C. Amazon S3
  • D. AWS Snowcone

Answer: C

Explanation:
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 Reference:
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.


NEW QUESTION # 65
A company built a deep learning model for object detection and deployed the model to production.
Which AI process occurs when the model analyzes a new image to identify objects?

  • A. Inference
  • B. Bias correction
  • C. Model deployment
  • D. Training

Answer: A

Explanation:
Inference is the correct answer because it is the AI process that occurs when a deployed model analyzes new data (such as an image) to make predictions or identify objects.
* Inference:
* In the context of machine learning, inference is the process of using a trained model to make predictions on new, unseen data.
* When the deep learning model is deployed to production and receives a new image for analysis, it uses the learned patterns from the training phase to identify objects in the image. This is known as inference.
* Why Option B is Correct:
* Inference Process: Involves applying the trained model to real-world data (the new image) to identify objects.
* Deployment Context: The model has already been trained, and the deployment to production indicates it is being used for inference.
* Why Other Options are Incorrect:
* A. Training: Refers to the process of teaching the model using historical data, not making predictions on new data.
* C. Model deployment: Refers to the process of making a trained model available for use in production.
* D. Bias correction: Is a process to adjust a model to minimize bias, not for analyzing new images.


NEW QUESTION # 66
A company is building a chatbot to improve user experience. The company is using a large language model (LLM) from Amazon Bedrock for intent detection. The company wants to use few-shot learning to improve intent detection accuracy.
Which additional data does the company need to meet these requirements?

  • A. Pairs of user intents and correct chatbot responses
  • B. Pairs of chatbot responses and correct user intents
  • C. Pairs of user messages and correct chatbot responses
  • D. Pairs of user messages and correct user intents

Answer: D


NEW QUESTION # 67
An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.
Which problem is the LLM having?

  • A. Overfitting
  • B. Underfitting
  • C. Hallucination
  • D. Data leakage

Answer: C

Explanation:
In the context of AI, "hallucination" refers to the phenomenon where a model generates outputs that are plausible-sounding but are not grounded in reality or the training data. This problem often occurs with large language models (LLMs) when they create information that sounds correct but is actually incorrect or fabricated.
* Option B (Correct): "Hallucination": This is the correct answer because the problem described involves generating content that sounds factual but is incorrect, which is characteristic of hallucination in generative AI models.
* Option A: "Data leakage" is incorrect as it involves the model accidentally learning from data it shouldn't have access to, which does not match the problem of generating incorrect content.
* Option C: "Overfitting" is incorrect because overfitting refers to a model that has learned the training data too well, including noise, and performs poorly on new data.
* Option D: "Underfitting" is incorrect because underfitting occurs when a model is too simple to capture the underlying patterns in the data, which is not the issue here.
AWS AI Practitioner References:
* Large Language Models on AWS: AWS discusses the challenge of hallucination in large language models and emphasizes techniques to mitigate it, such as using guardrails and fine-tuning.


NEW QUESTION # 68
A company wants to develop an educational game where users answer questions such as the following: "A jar contains six red, four green, and three yellow marbles. What is the probability of choosing a green marble from the jar?" Which solution meets these requirements with the LEAST operational overhead?

  • A. Use code that will calculate probability by using simple rules and computations.
  • B. Use supervised learning to create a regression model that will predict probability.
  • C. Use unsupervised learning to create a model that will estimate probability density.
  • D. Use reinforcement learning to train a model to return the probability.

Answer: A


NEW QUESTION # 69
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