Which Vertex AI Explanation Method Should You Use for Mislabeled Image Predictions?

PMLE By islever Editorial Last updated Aug 31, 2026

Short answer

D Configure example-based explanations. Specify the embedding output layer to be used for the latent space representation.

Question

You work for a manufacturing company. You need to train a custom image classification model to detect product defects at the end of an assembly line. Although your model is performing well, some images in your holdout set are consistently mislabeled with high confidence. You want to use Vertex AI to understand your model’s results. What should you do?

Configure feature-based explanations by using Integrated Gradients. Set visualization type to PIXELS, and set clip_percent_upperbound to 95.
Create an index by using Vertex AI Matching Engine. Query the index with your mislabeled images.
Configure feature-based explanations by using XRAI. Set visualization type to OUTLINES, and set polarity to positive.
Configure example-based explanations. Specify the embedding output layer to be used for the latent space representation. Correct Answer

Key Takeaways

The question tests whether you understand the difference between feature-based and example-based explanations, with the trap being to assume pixel-level heatmaps are always the right way to debug misclassifications.

For interpreting consistently mislabeled image predictions in Vertex AI, example-based explanations help uncover why the model makes specific mistakes by comparing them to similar training examples. Community consensus favors example-based methods over feature-based saliency maps like Integrated Gradients or XRAI for this use case.

Common mistake: Selecting A (Integrated Gradients with PIXELS) is the most common wrong answer because it is a well-known Vertex AI explanation method, but it only shows which pixels influenced a prediction rather than explaining why similar images were mislabeled the same way.

Expert Analysis

Why the Answer Is Correct

Option D is correct because the scenario explicitly asks to understand why the model consistently mislabels certain images with high confidence. Example-based explanations in Vertex AI let you inspect the training examples that are most similar to a misclassified query image, revealing patterns in the data or model behavior that led to the error. As the official documentation states, one of the core use cases is to "improve your data or model" by understanding mistakes in predictions. Specifying the embedding output layer is required to define the latent space used for similarity search.

Why the Other Options Are Wrong

Option A is not the best choice because Integrated Gradients provides feature-attribution explanations, showing pixel-level importance, but it does not directly help you understand why a mislabeled image belongs to a specific wrong class. While pixel visualizations can be helpful, they do not address the root cause of consistent mislabeling with high confidence. Option B incorrectly suggests using Matching Engine, which is a vector similarity search service, not an explanation tool; it does not generate explanations for model predictions. Option C is invalid because XRAI does not support setting visualization type to OUTLINES, and its polarity setting is not used in the way described in this option.

Community Comment Notes

Several commenters correctly selected D and referenced the Vertex AI Explainable AI overview, noting that example-based explanations are specifically designed for understanding model mistakes and improving data or model quality. One commenter with the most upvoted answer said, "It is to understand why model is making specific mistakes, so example-based explanation makes sense." The comments for A were based on the idea that Integrated Gradients is a common method for image models, but they did not address the core requirement of understanding misclassifications. The official documentation for visualization settings confirms that some options like XRAI outlines are not supported, helping eliminate option C.

Official Reference

These links open the official vendor documentation in a new tab.

Exam Strategy

When a question asks why a model makes mistakes or how to improve a model using insights from predictions, look for example-based explanations rather than feature-based saliency methods. Remember that feature-based explanations only highlight input features, while example-based explanations compare predictions to similar examples and are tailored for debugging and data improvement.

Community Votes

60%

40%

60% of anonymous learners picked answer D. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 7 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 7

guilhermebutzke 👍 4

My Answer: A According to this documentation: https://cloud.google.com/vertex-ai/docs/explainable-ai/visualization-settings This option A aligns with using Integrated Gradients, which is suitable for feature-based explanations. Setting the visualization type to PIXELS allows for per-pixel attribution, which can help in understanding the specific regions of the image influencing the model's decision. Additionally, setting the clip_percent_upperbound parameter to 95 helps in filtering out noise and focusing on areas of strong attribution, which is crucial for understanding mislabeled images with high confidence. Option C suggests using XRAI for feature-based explanations and setting the visualization type to OUTLINES, along with setting the polarity to positive. However, based on the provided documentation, XRAI is recommended to have its visualization type set to PIXELS, not OUTLINES.
VipinSingla 👍 4 Answered D

Improve your data or model: One of the core use cases for example-based explanations is helping you understand why your model made certain mistakes in its predictions, and using those insights to improve your data or model. https://cloud.google.com/vertex-ai/docs/explainable-ai/overview
eico 👍 3 Answered D

https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#example-based "Improve your data or model: One of the core use cases for example-based explanations is helping you understand why your model made certain mistakes in its predictions, and using those insights to improve your data or model. [...] For example, suppose we have a model that classifies images as either a bird or a plane, and that it is misclassifying the following bird as a plane with high confidence. You can use Example-based explanations to retrieve similar images from the training set to figure out what is happening." Not A: Integrated Gradients is recommended for low-contrast images, such as X-rays https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#compare-methods Not C: Cannot set Outlines for XRAI https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/visualizing-explanations
shadz10 👍 2 Answered A

Going with A Not c - For XRAI, Pixels is the default setting and shows areas of attribution. Outlines is not recommended for XRAI. https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/visualizing-explanations
sonicclasps 👍 2 Answered A

Although Xrai could be an option, it doesn't not allow you to set those options, so only other answer is A https://cloud.google.com/vertex-ai/docs/explainable-ai/visualization-settings#visualization_options

Show 2 more comments

rajshiv 👍 2 Answered A

A is the best answer among the choices. Integrated gradient and pixel visualization is the best alternative among the choices given.
YangG 👍 2 Answered D

It is to understand why model is making specific mistakes, so example-based explanation makes sense to me.

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