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Amazon AIF-C01 Exam - Topic 5 Question 21 Discussion

A company deployed an AI/ML solution to help customer service agents respond to frequently asked questions. The questions can change over time. The company wants to give customer service agents the ability to ask questions and receive automatically generated answers to common customer questions. Which strategy will meet these requirements MOST cost-effectively?
A) Fine-tune the model regularly.
B) Train the model by using context data.
C) Pre-train and benchmark the model by using context data.
D) Use Retrieval Augmented Generation (RAG) with prompt engineering techniques.

Amazon AIF-C01 Exam - Topic 5 Question 21 Discussion

Actual exam question for Amazon's AIF-C01 exam
Question #: 21
Topic #: 5
[All AIF-C01 Questions]

A company deployed an AI/ML solution to help customer service agents respond to frequently asked questions. The questions can change over time. The company wants to give customer service agents the ability to ask questions and receive automatically generated answers to common customer questions. Which strategy will meet these requirements MOST cost-effectively?

Show Suggested Answer Hide Answer
Suggested Answer: A

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 Reference:

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.


by Emile at Mar 22, 2025, 02:23 AM

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Ricarda
5 months ago
Not sure if RAG is the most efficient for this.
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Maxima
5 months ago
Wait, can RAG really handle changing questions effectively?
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Glen
5 months ago
Pre-training seems like a solid approach, but I wonder if it really addresses the dynamic nature of customer questions effectively.
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Catarina
5 months ago
RAG sounds familiar from our practice questions, but I can't recall if it's the most cost-effective option.
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Chaya
5 months ago
I think option D is the best choice!
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Hui
6 months ago
I think using context data is important, but I'm not sure if just training the model is enough for changing questions.
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Sharika
6 months ago
I remember discussing how fine-tuning can be resource-intensive, but it might be necessary for accuracy.
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Cheryl
6 months ago
Fine-tuning can get expensive over time.
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Eugene
6 months ago
Totally agree with D, it sounds cost-effective!
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Luisa
6 months ago
I'm feeling pretty confident about this one. I think the fine-tuning approach is the way to go. It should allow the model to adapt to the changing questions without too much overhead.
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Werner
7 months ago
I think I'm leaning towards the pre-training and benchmarking option. That seems like it could provide a solid foundation for handling the changing questions, while still being relatively cost-effective.
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Marylou
7 months ago
Hmm, the Retrieval Augmented Generation (RAG) with prompt engineering techniques sounds interesting. I wonder if that would be the most cost-effective solution in the long run, even if it requires more upfront work.
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Ryann
7 months ago
I'm a bit confused by the options here. Training the model using context data seems like it could be a good approach, but I'm not sure how that would compare to pre-training and benchmarking the model.
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Tony
7 months ago
This seems like a tricky one. I'm not sure if fine-tuning the model regularly is the most cost-effective approach, but it could be a good option if the questions change frequently.
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Fabiola
7 months ago
This is a good one. I'm leaning towards option D, using RAG with prompt engineering. It sounds like it could be a powerful way to handle the changing questions while keeping costs down. I'll need to do some research on that approach, but I think it's worth considering.
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Stefany
8 months ago
Okay, I think I've got a strategy here. Since the questions can change over time, pre-training and benchmarking the model using context data seems like the most flexible and cost-effective approach. I'll go with option C.
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Phung
8 months ago
Hmm, I'm a bit confused by the options here. I'm not familiar with Retrieval Augmented Generation (RAG) or prompt engineering techniques. I'll have to look into those more before deciding.
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Kristel
8 months ago
This seems like a tricky question. I'm not sure if fine-tuning the model regularly or using context data would be the most cost-effective approach. I'll need to think this through carefully.
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Diane
1 year ago
Why not just hire a team of psychic customer service agents? They could predict the questions before they even come in!
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Jade
1 year ago
I'd recommend going with the 'Ask Jeeves' method. That always worked for me back in the day!
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Gertude
12 months ago
That's a good point. Chatbots can adapt to changing questions.
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Dudley
12 months ago
Chatbots can be trained to answer common questions.
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Katy
12 months ago
I think using a chatbot could be cost-effective.
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Tien
12 months ago
Ask Jeeves method sounds interesting.
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Valentin
1 year ago
Option A is definitely the easiest, but I can't imagine the company wants to be constantly fine-tuning the model. That sounds like a lot of work! I'd go with D - it's the most future-proof solution.
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Hildegarde
12 months ago
Yeah, Option D would probably save time and resources in the long run. It's important to consider the scalability and sustainability of the solution.
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Alishia
12 months ago
I agree, constantly fine-tuning the model could be time-consuming. Option D does seem like a more future-proof solution.
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Daren
1 year ago
Option A is definitely the easiest, but I can't imagine the company wants to be constantly fine-tuning the model. That sounds like a lot of work!
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Launa
1 year ago
I'm leaning towards B. Using context data to train the model could make it more adaptable to the changing questions. Plus, it's probably more cost-effective than constantly fine-tuning the model.
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Nikita
1 year ago
Option C seems promising, but I'm not sure if pre-training and benchmarking will be enough to keep up with the changing questions. Maybe a combination of C and D would be best?
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Georgiana
12 months ago
We should consider the scalability and adaptability of the solution to handle changing questions efficiently.
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Maryrose
1 year ago
I agree, a combination of pre-training and benchmarking with continuous learning could be the best approach.
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Isadora
1 year ago
Option C could be a good starting point, but combining it with D might be more effective.
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Reta
1 year ago
I think option D is the way to go. Retrieval Augmented Generation with prompt engineering sounds like it would give the agents the most up-to-date and relevant responses without a lot of manual effort.
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Elke
12 months ago
Using retrieval augmented generation with prompt engineering sounds like a smart approach.
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Andree
1 year ago
I think fine-tuning the model regularly could also be effective.
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Anabel
1 year ago
I agree, option D seems like the most efficient choice.
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Kasandra
1 year ago
I believe using Retrieval Augmented Generation (RAG) with prompt engineering techniques would be the most cost-effective option.
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Fernanda
1 year ago
I agree with Luisa. It's important to keep the model updated with new data.
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Luisa
1 year ago
I think we should fine-tune the model regularly.
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