- A
A trained model checkpoint.
Why wrong: Checkpoints are for model weights, not preprocessing.
- B
A analyzed_dataset directory with statistics.
Why wrong: Statistics are for analysis, not for applying transformations.
- C
A flattened schema file (schema.pbtxt).
Why wrong: Schema describes the data format, not the transformation logic.
- D
A transform_fn SavedModel.
The transform_fn is the output of tf.Transform that applies the same transformation to new data.
Question 73 of 1,000
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml 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 team is using TensorFlow Transform (tf.Transform) to create preprocessing functions that will be used both in training and serving. They want to ensure consistency. Which artifact should they save after analyzing the training data?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A transform_fn SavedModel.
The correct artifact to save after analyzing training data with tf.Transform is the `transform_fn` SavedModel. This SavedModel encapsulates the exact preprocessing logic (e.g., scaling, normalization, vocabulary mapping) computed from the training dataset, ensuring that the same transformations are applied consistently during both training and serving. Without this artifact, the serving pipeline would need to recompute or duplicate the transformation logic, risking skew between training and inference.
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.
- ✗
A trained model checkpoint.
Why it's wrong here
Checkpoints are for model weights, not preprocessing.
- ✗
A analyzed_dataset directory with statistics.
Why it's wrong here
Statistics are for analysis, not for applying transformations.
- ✗
A flattened schema file (schema.pbtxt).
Why it's wrong here
Schema describes the data format, not the transformation logic.
- ✓
A transform_fn SavedModel.
Why this is correct
The transform_fn is the output of tf.Transform that applies the same transformation to new data.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common pitfall is to confuse intermediate analysis outputs (like statistics or schema) with the executable artifact (the SavedModel) that actually applies the transformation, leading candidates to mistakenly select the schema or statistics directory as the key artifact.
Detailed technical explanation
How to think about this question
Under the hood, tf.Transform uses Apache Beam to compute global statistics (e.g., min, max, mean) over the entire training dataset, then generates a TensorFlow graph that applies these statistics as fixed constants. This graph is exported as a `transform_fn` SavedModel, which can be loaded and run in both training (via `tf.Transform`'s `preprocessing_fn`) and serving (via TensorFlow Serving or a custom inference pipeline) without recomputing the statistics. A subtle behavior is that the `transform_fn` must be applied in the same order and with the same data types as during analysis; otherwise, feature skew can silently degrade model performance.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Questions learners often ask
What does this PMLE question test?
Scaling Prototypes into ML Models — This question tests Scaling Prototypes into ML Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: A transform_fn SavedModel. — The correct artifact to save after analyzing training data with tf.Transform is the `transform_fn` SavedModel. This SavedModel encapsulates the exact preprocessing logic (e.g., scaling, normalization, vocabulary mapping) computed from the training dataset, ensuring that the same transformations are applied consistently during both training and serving. Without this artifact, the serving pipeline would need to recompute or duplicate the transformation logic, risking skew between training and inference.
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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Last reviewed: Jul 4, 2026
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.
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