Quick Answer
The answer is to add more labeled images to the dataset. This is the correct first step because Vertex AI AutoML vision models rely on transfer learning from pre-trained architectures, meaning their performance is directly tied to the quantity and representativeness of the training examples—low accuracy almost always stems from insufficient or unbalanced labeled data, not from model architecture or hyperparameters. On the Google Professional Machine Learning Engineer exam, this question tests your understanding that AutoML handles tuning automatically, so the common trap is jumping to adjust training budgets or infrastructure instead of fixing the data foundation. A useful memory tip is “data before dials”—always enrich your dataset before touching any configuration settings, as AutoML is designed to optimize the model once it has enough high-quality examples to learn from.
This PMLE practice question tests your understanding of architecting low-code ml solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company uses Vertex AI AutoML to train a vision model, but the model has low accuracy. What should they do first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Add more labeled images to the dataset
More data often improves model accuracy.
Switch to a custom model
Why wrong: This moves away from low-code and may not fix data issues.
Increase the training budget
Why wrong: Increasing budget may not help if the data is insufficient.
Reduce image size to speed up training
Why wrong: Reducing size can lose information and hurt accuracy.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Add more labeled images to the dataset
Adding more labeled images directly addresses the most common cause of low accuracy in AutoML vision models: insufficient or unrepresentative training data. Vertex AI AutoML relies on transfer learning from pre-trained models, and its performance is heavily dependent on the quality and quantity of labeled examples. Before adjusting hyperparameters or infrastructure, the first step should always be to improve the dataset, as AutoML is designed to handle model architecture and training budget automatically.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Add more labeled images to the dataset
Why this is correct
More data often improves model accuracy.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Switch to a custom model
Why it's wrong here
This moves away from low-code and may not fix data issues.
Increase the training budget
Why it's wrong here
Increasing budget may not help if the data is insufficient.
Reduce image size to speed up training
Why it's wrong here
Reducing size can lose information and hurt accuracy.
Common exam traps
Google Cloud often tests the misconception that AutoML models are 'black boxes' where tuning budgets or switching to custom models is the first fix, when in reality the platform is optimized to handle those aspects automatically, and the primary lever is data quality.
Detailed technical explanation
Vertex AI AutoML uses a pre-trained EfficientNet backbone and applies neural architecture search (NAS) to fine-tune the model for the specific dataset. The model's accuracy is bounded by the information content of the training data; adding more diverse, correctly labeled images reduces overfitting and improves generalization. In practice, a common rule of thumb is to have at least 1,000 labeled images per class for reliable results, and AutoML's confidence calibration also benefits from larger datasets.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Add more labeled images to the dataset — Adding more labeled images directly addresses the most common cause of low accuracy in AutoML vision models: insufficient or unrepresentative training data. Vertex AI AutoML relies on transfer learning from pre-trained models, and its performance is heavily dependent on the quality and quantity of labeled examples. Before adjusting hyperparameters or infrastructure, the first step should always be to improve the dataset, as AutoML is designed to handle model architecture and training budget automatically.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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