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Amazon AIF-C01 Exam - Topic 1 Question 25 Discussion

A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts.An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders.What should the AI practitioner include in the report to meet the transparency and explainability requirements?
B) Partial dependence plots (PDPs)
A) Code for model training
C) Sample data for training
D) Model convergence tables

Amazon AIF-C01 Exam - Topic 1 Question 25 Discussion

Actual exam question for Amazon's AIF-C01 exam
Question #: 25
Topic #: 1
[All AIF-C01 Questions]

A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts.

An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders.

What should the AI practitioner include in the report to meet the transparency and explainability requirements?

Show Suggested Answer Hide Answer
Suggested Answer: B

Partial dependence plots (PDPs) are visual tools used to show the relationship between a feature (or a set of features) in the data and the predicted outcome of a machine learning model. They are highly effective for providing transparency and explainability of the model's behavior to stakeholders by illustrating how different input variables impact the model's predictions.

Option B (Correct): 'Partial dependence plots (PDPs)': This is the correct answer because PDPs help to interpret how the model's predictions change with varying values of input features, providing stakeholders with a clearer understanding of the model's decision-making process.

Option A: 'Code for model training' is incorrect because providing the raw code for model training may not offer transparency or explainability to non-technical stakeholders.

Option C: 'Sample data for training' is incorrect as sample data alone does not explain how the model works or its decision-making process.

Option D: 'Model convergence tables' is incorrect. While convergence tables can show the training process, they do not provide insights into how input features affect the model's predictions.

AWS AI Practitioner Reference:

Explainability in AWS Machine Learning: AWS provides various tools for model explainability, such as Amazon SageMaker Clarify, which includes PDPs to help explain the impact of different features on the model's predictions.


by Gianna at Jul 05, 2025, 07:44 PM

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Antonio
6 months ago
Sample data? Isn't that a bit risky to share?
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Tonette
6 months ago
I think code for model training should definitely be included too.
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Buck
6 months ago
Wait, are PDPs really enough for transparency? Sounds too simple.
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Junita
6 months ago
Totally agree with including model convergence tables!
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Yoko
6 months ago
PDPs are super important for understanding model behavior.
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Nickole
7 months ago
I practiced a question similar to this, and I think including the code for model training could be important for transparency, but it might be too detailed for the report.
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Luann
7 months ago
I feel like model convergence tables might be useful to show how well the model is performing, but they seem a bit technical for non-experts.
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Dawne
7 months ago
I think providing sample data for training could help stakeholders understand the model's context, but I wonder if it might raise privacy concerns.
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Maurine
7 months ago
I remember we discussed the importance of including visualizations like PDPs for explaining model behavior, but I'm not entirely sure if that's the best choice here.
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Fairy
7 months ago
I'm leaning towards the model convergence tables. That would give the stakeholders insight into how well the models are performing and whether they're converging properly. Plus, it's a more technical detail that shows the rigor of the modeling process.
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Melissa
8 months ago
Okay, I've got this. The report should include partial dependence plots to show how the key features impact the model's predictions. That will help the stakeholders understand the model's logic and decision-making process.
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Arlette
8 months ago
Hmm, I'm a bit unsure about this one. I know we need to explain the models, but I'm not sure if the code or sample data is the best approach. Maybe something like partial dependence plots would be more helpful for stakeholders?
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Tammara
8 months ago
This seems like a straightforward question about transparency and explainability for ML models. I think the key is to focus on providing information that helps stakeholders understand how the models work and make decisions.
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Rasheeda
10 months ago
I bet the stakeholders are just going to flip a coin to decide the answer. 'Heads, we go with the code. Tails, we use the sample data.' Either way, they'll probably end up just as confused as before.
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Brianne
8 months ago
B: I think including sample data for training would also help meet the transparency and explainability requirements.
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Sommer
8 months ago
A: The AI practitioner should include partial dependence plots (PDPs) in the report.
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Luisa
10 months ago
B) Partial dependence plots (PDPs) for sure. It's the perfect balance of technical detail and visual explanation. Plus, it's way easier to understand than that convergence table mumbo-jumbo.
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Thaddeus
10 months ago
B: Yeah, and it helps stakeholders understand the model's decision-making process better.
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Sarah
10 months ago
A: Definitely agree, PDPs are great for showing how the model's predictions change with different input values.
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Mitzie
10 months ago
A) Code for model training? Really? That's way too technical for a stakeholder report. No one wants to see that mess of code!
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Antonio
10 months ago
D) Model convergence tables might be too technical for stakeholders, they might not understand the significance of those.
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Lynette
10 months ago
C) Including sample data for training would also help stakeholders see the inputs and outputs of the model.
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Avery
10 months ago
B) Partial dependence plots (PDPs) would be more useful for stakeholders to understand how the model makes predictions.
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Veronika
11 months ago
I think sample data for training should be included to demonstrate the quality of data used in training the models.
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Haley
11 months ago
Personally, I'm leaning towards C) Sample data for training. Seeing the actual data used to train the model would be great for explainability.
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Devora
10 months ago
I think including partial dependence plots (PDPs) could also be useful in explaining how the model makes predictions.
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Brandon
10 months ago
I agree, having access to the sample data would definitely help stakeholders understand how the model was trained.
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Ellen
11 months ago
I believe partial dependence plots (PDPs) should also be included to show the impact of each feature on the forecasts.
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Glory
11 months ago
Hmm, I'm not sure. I think it might be D) Model convergence tables. Seeing how the model converged during training could be really useful for transparency.
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Maynard
10 months ago
I agree, it would show how the model progressed during training.
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Fausto
10 months ago
I think including model convergence tables would definitely help with transparency.
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Paris
11 months ago
I agree with Michel, including the code will help stakeholders understand how the models were trained.
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Kimberlie
11 months ago
I think the answer is B) Partial dependence plots (PDPs). This will help stakeholders understand how the features in the model influence the predictions.
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Eve
11 months ago
C: It's important to provide clear explanations to stakeholders so they can trust the model's forecasts.
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Kent
11 months ago
B: I agree, PDPs are a great way to show the relationship between input features and the model's predictions.
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Goldie
11 months ago
A: I think the answer is B) Partial dependence plots (PDPs). This will help stakeholders understand how the features in the model influence the predictions.
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Michel
11 months ago
I think the AI practitioner should include code for model training in the report.
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