This PMLE practice question tests your understanding of serving and scaling models. 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 trains a model using Vertex AI Training and then deploys it to Vertex AI Prediction. They notice that prediction requests fail with 'InvalidArgument: input tensor shape mismatch'. Which THREE are possible causes?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The input data types do not match the expected types (e.g., float vs int)
Option C is correct because Vertex AI Prediction expects the input tensor data types to exactly match those used during model training. If the model was trained with float32 inputs but the prediction request sends int32 values, the serving infrastructure detects the mismatch and returns an 'InvalidArgument: input tensor shape mismatch' error, as TensorFlow Serving (which underlies Vertex AI Prediction) validates dtype consistency at the graph level.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
The model was exported in a different format than supported
Why it's wrong here
Vertex AI supports multiple formats; format is handled automatically.
✗
The batch size in the request is too large
Why it's wrong here
Batch size is a dimension, but models accept variable batch sizes.
✓
The input data types do not match the expected types (e.g., float vs int)
Why this is correct
Data type mismatch causes shape or value errors.
Related concept
Read the scenario before looking for a memorised answer.
✓
The input data has a different number of features than the model expects
Why this is correct
Feature count mismatch causes shape error.
Related concept
Read the scenario before looking for a memorised answer.
✓
The serving function does not include the same preprocessing as training
Why this is correct
Preprocessing mismatch leads to shape errors.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that 'shape mismatch' only refers to the number of features or dimensions, when in fact it also encompasses data type mismatches and preprocessing inconsistencies that alter the tensor structure before it reaches the model.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Prediction uses TensorFlow Serving's Predict API, which performs strict shape and dtype validation against the model's signature_def. If the serving input_fn or preprocessing pipeline (e.g., in a custom container) applies transformations like scaling or one-hot encoding that differ from training, the resulting tensor shapes can diverge—e.g., training expects a 784-element vector but the serving function outputs a 785-element vector due to an extra bias term. This is a common pitfall when using tf.Transform or Keras preprocessing layers that are not serialized identically.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Serving and scaling models — This question tests Serving and scaling models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The input data types do not match the expected types (e.g., float vs int) — Option C is correct because Vertex AI Prediction expects the input tensor data types to exactly match those used during model training. If the model was trained with float32 inputs but the prediction request sends int32 values, the serving infrastructure detects the mismatch and returns an 'InvalidArgument: input tensor shape mismatch' error, as TensorFlow Serving (which underlies Vertex AI Prediction) validates dtype consistency at the graph level.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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