- A
Require manual approval for every model change before deployment
Why wrong: Manual approval can be used for production but is not a best practice for every change as it introduces delays.
- B
Store all model artifacts in a single Cloud Storage bucket without versioning
Why wrong: Without versioning, reproducibility is lost.
- C
Use immutable container images with version tags for each model deployment
Immutable images ensure that the exact same environment is used across all deployments.
- D
Include unit tests for data preprocessing and feature engineering code in the pipeline
Testing ensures that data transformations are correct and reproducible.
- E
Deploy every model version directly to production for immediate use
Why wrong: Best practice is to deploy to staging first for validation.
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 is designing a CI/CD pipeline for their ML models using Cloud Build and Vertex AI. Which TWO practices should they adopt to ensure reliable and reproducible deployments?
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
Use immutable container images with version tags for each model deployment
Option C is correct because using immutable container images with version tags ensures that each deployment is based on a fixed, unchangeable artifact. This eliminates the risk of configuration drift or unintended changes between builds, which is critical for reproducibility in Vertex AI deployments. Cloud Build can tag images with the commit SHA or build ID, making each deployment traceable and rollback-safe.
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.
- ✗
Require manual approval for every model change before deployment
Why it's wrong here
Manual approval can be used for production but is not a best practice for every change as it introduces delays.
- ✗
Store all model artifacts in a single Cloud Storage bucket without versioning
Why it's wrong here
Without versioning, reproducibility is lost.
- ✓
Use immutable container images with version tags for each model deployment
Why this is correct
Immutable images ensure that the exact same environment is used across all deployments.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Include unit tests for data preprocessing and feature engineering code in the pipeline
Why this is correct
Testing ensures that data transformations are correct and reproducible.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploy every model version directly to production for immediate use
Why it's wrong here
Best practice is to deploy to staging first for validation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that manual approval gates or single-bucket storage without versioning are acceptable for reproducibility, when in fact they undermine automation and traceability in CI/CD pipelines.
Detailed technical explanation
How to think about this question
Immutable container images rely on content-addressable storage, where the image digest (SHA256 hash) uniquely identifies the exact filesystem layers. In Cloud Build, using `gcr.io/cloud-builders/docker` with `--tag` and `--digest` flags ensures that the image pushed to Artifact Registry is immutable. Vertex AI supports deploying models from these images via the `aiplatform.Model` resource, where the `imageUri` includes the digest for deterministic deployments. A real-world scenario: a data scientist updates a preprocessing script but forgets to rebuild the container; without immutability, the old image might be reused, causing silent prediction errors.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
- →
Operationalizing machine learning models — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use immutable container images with version tags for each model deployment — Option C is correct because using immutable container images with version tags ensures that each deployment is based on a fixed, unchangeable artifact. This eliminates the risk of configuration drift or unintended changes between builds, which is critical for reproducibility in Vertex AI deployments. Cloud Build can tag images with the commit SHA or build ID, making each deployment traceable and rollback-safe.
What should I do if I get this PDE 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
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