The answer is to modify the client code to send requests with the input key 'text'. This is correct because Vertex AI Prediction serves the deployed model as-is, meaning the model’s serving signature expects the exact tensor name ‘text’ from the input JSON; any mismatch, such as ‘review_text’, will cause a key error since the model’s computational graph has no mapping for the alternative key. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding that aligning client payloads with the model’s saved signature is the simplest, most maintainable fix—avoiding unnecessary retraining, custom prediction routines, or endpoint reconfiguration. A common trap is overcomplicating the solution by suggesting input preprocessing or model wrapping, when the direct client-side alignment is both faster and less error-prone. Remember the memory tip: “Match the key, no need to retrain—just align the client’s lane.”
PMLE Solving business challenges with ML Practice Question
This PMLE practice question tests your understanding of solving business challenges with ml. 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.
Exhibit
Refer to the exhibit.
```
Error: INVALID_ARGUMENT: Model 'projects/my-project/models/sentiment_v2' failed to load. The model's signature definition does not match the prediction request.
Expected: input: 'text' (string), output: 'scores' (float array)
Received: input: 'review_text' (string)
```
A data scientist deployed a TensorFlow model for sentiment analysis to Vertex AI Prediction. The model expects input key 'text' but the client sends requests with key 'review_text'. Which step should the data scientist take to resolve the error without retraining the model?
Refer to the exhibit.
```
Error: INVALID_ARGUMENT: Model 'projects/my-project/models/sentiment_v2' failed to load. The model's signature definition does not match the prediction request.
Expected: input: 'text' (string), output: 'scores' (float array)
Received: input: 'review_text' (string)
```
A
Use a Cloud Function to strip the 'review_text' key and replace it with 'text'
Why wrong: Adding a Cloud Function adds latency and complexity; a simpler client-side fix is better.
B
Retrain the model with input key 'review_text'
Why wrong: Retraining is unnecessary and time-consuming; the model is already correct.
C
Create a new Vertex AI Endpoint with an alias mapping 'review_text' to 'text'
Why wrong: Vertex AI does not support input key aliasing at the endpoint level.
D
Modify the client code to send requests with input key 'text'
This aligns the request with the model's expected signature without changing the model.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Modify the client code to send requests with input key 'text'
Option D is correct because the most straightforward and reliable solution is to modify the client code to send the request with the expected input key 'text'. This avoids any additional infrastructure, latency, or complexity, and does not require retraining the model or altering the deployed endpoint. Vertex AI Prediction serves the model as-is, so aligning the client's request format with the model's expected input is the simplest and most maintainable fix.
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.
✗
Use a Cloud Function to strip the 'review_text' key and replace it with 'text'
Why it's wrong here
Adding a Cloud Function adds latency and complexity; a simpler client-side fix is better.
✗
Retrain the model with input key 'review_text'
Why it's wrong here
Retraining is unnecessary and time-consuming; the model is already correct.
✗
Create a new Vertex AI Endpoint with an alias mapping 'review_text' to 'text'
Why it's wrong here
Vertex AI does not support input key aliasing at the endpoint level.
✓
Modify the client code to send requests with input key 'text'
Why this is correct
This aligns the request with the model's expected signature without changing the model.
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 you need to add infrastructure (like Cloud Functions) or modify the model to handle input key mismatches, when the correct answer is to adjust the client code to match the model's expected input schema.
Detailed technical explanation
How to think about this question
When a TensorFlow model is deployed to Vertex AI Prediction, the serving infrastructure uses the model's saved signature (defined in the SavedModel) to parse incoming JSON requests. The input key must exactly match the key defined in the model's serving_input_receiver_fn (for custom models) or the default serving signature. Renaming the key in the client code is the only way to match this fixed signature without modifying the model or adding a preprocessing layer. In production, this is often handled via a lightweight API gateway or a model wrapper that performs key mapping, but the simplest and most direct approach is to fix the client.
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.
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Modify the client code to send requests with input key 'text' — Option D is correct because the most straightforward and reliable solution is to modify the client code to send the request with the expected input key 'text'. This avoids any additional infrastructure, latency, or complexity, and does not require retraining the model or altering the deployed endpoint. Vertex AI Prediction serves the model as-is, so aligning the client's request format with the model's expected input is the simplest and most maintainable fix.
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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