KEY AIF-C01 CONCEPTS | AIF-C01 VCE DUMPS

Key AIF-C01 Concepts | AIF-C01 VCE Dumps

Key AIF-C01 Concepts | AIF-C01 VCE Dumps

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Tags: Key AIF-C01 Concepts, AIF-C01 VCE Dumps, AIF-C01 Certification Test Answers, Hot AIF-C01 Questions, AIF-C01 Guaranteed Questions Answers

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Amazon AWS Certified AI Practitioner Sample Questions (Q47-Q52):

NEW QUESTION # 47
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 user messages and correct user intents
  • C. Pairs of chatbot responses and correct user intents
  • D. Pairs of user messages and correct chatbot responses

Answer: B

Explanation:
Few-shot learning involves providing a model with a few examples (shots) to learn from. For improving intent detection accuracy in a chatbot using a large language model (LLM), the data should consist of pairs of user messages and their corresponding correct intents.
* Few-shot Learning for Intent Detection:
* Few-shot learning aims to enable the model to learn from a small number of examples. For intent detection, the model needs to understand the relationship between user messages and the intended action or meaning.
* Providing examples of user messages and the correct user intents allows the model to learn patterns in the phrasing or language that corresponds to each intent.
* Why Option C is Correct:
* User Messages and Intents: These examples directly teach the model how to map a user's input to the appropriate intent, which is the goal of intent detection in chatbots.
* Improves Accuracy: By using few-shot learning with these examples, the model can generalize better from limited data, improving intent detection.
* Why Other Options are Incorrect:
* A. Pairs of chatbot responses and correct user intents: Incorrect because it does not focus on user input but rather on outputs.
* B. Pairs of user messages and correct chatbot responses: This would be useful for response generation, not intent detection.
* D. Pairs of user intents and correct chatbot responses: Again, this is not aligned with detecting intents but with generating responses.


NEW QUESTION # 48
A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost.
Which solution will meet these requirements?

  • A. Use Provisioned Throughput.
  • B. Decrease the number of tokens in the prompt.
  • C. Customize the model by using fine-tuning.
  • D. Increase the number of tokens in the prompt.

Answer: B

Explanation:
Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.
* Token Reduction Strategy:
* By decreasing the number of tokens (words or characters) in each prompt, the company reduces the computational load and, therefore, the cost associated with invoking the model.
* Since the model is performing well with few-shot prompting, reducing token usage without sacrificing performance can lower monthly costs.
* Why Option B is Correct:
* Cost Efficiency: Directly reduces the number of tokens processed, lowering costs without requiring additional adjustments.
* Maintaining Performance: If the model is already performing well, a reduction in tokens should not significantly impact its performance.
* Why Other Options are Incorrect:
* A. Fine-tuning: Can be costly and time-consuming and is not needed if the current model is already performing well.
* C. Increase the number of tokens: Would increase costs, not lower them.
* D. Use Provisioned Throughput: Is unrelated to token costs and applies more to read/write capacity in databases.


NEW QUESTION # 49
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. Training
  • B. Bias correction
  • C. Inference
  • D. Model deployment

Answer: C


NEW QUESTION # 50
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?

  • A. Exploratory data analysis
  • B. Feature engineering
  • C. Hyperparameter tuning
  • D. Data pre-processing

Answer: A

Explanation:
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
* Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
* Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
* Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.
* Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner References:
* Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.


NEW QUESTION # 51
A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.
Which ML algorithm meets these requirements?

  • A. Logistic regression
  • B. Decision trees
  • C. Neural networks
  • D. Linear regression

Answer: B

Explanation:
Decision trees are an interpretable machine learning algorithm that clearly documents the decision-making process by showing how each input feature affects the output. This transparency is particularly useful when explaining how the model arrives at a certain decision, making it suitable for classifying genes into categories.
* Option A (Correct): "Decision trees": This is the correct answer because decision trees provide a clear and interpretable representation of how input features influence the model's output, making it ideal for understanding the inner mechanisms affecting predictions.
* Option B: "Linear regression" is incorrect because it is used for regression tasks, not classification.
* Option C: "Logistic regression" is incorrect as it does not provide the same level of interpretability in documenting decision-making processes.
* Option D: "Neural networks" is incorrect because they are often considered "black boxes" and do not easily explain how they arrive at their outputs.
AWS AI Practitioner References:
* Interpretable Machine Learning Models on AWS: AWS supports using interpretable models, such as decision trees, for tasks that require clear documentation of how input data affects output decisions.


NEW QUESTION # 52
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