The answer is locking dependency versions in a container image and version controlling datasets using DVC or Vertex AI ML Metadata. Locking dependencies in a custom container ensures the exact same software environment—including Python packages, CUDA libraries, and system tools—is used every time a training job runs, eliminating variability from updates or patches. Versioning datasets captures the exact data snapshot used, preventing silent drift that breaks reproducibility across teams. On the Google Professional Machine Learning Engineer exam, this question tests your understanding that reproducibility requires both environment and data immutability; a common trap is choosing only one of these or relying on default Vertex AI training containers, which can change. Remember the mnemonic "Lock and Track"—lock your container image, track your dataset version—to keep every experiment repeatable.
PMLE Practice Question: Collaborating within and across teams to manage data and models
This PMLE practice question tests your understanding of collaborating within and across teams to manage data and 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.
Which TWO actions should be taken to ensure reproducibility of ML experiments when collaborating across teams on Vertex AI?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Lock dependency versions in a container image used for training
Locking dependency versions in a container image ensures that the exact same software environment (e.g., Python packages, CUDA libraries, system tools) is used every time a training job runs. This eliminates variability from package updates or OS patches, which is a fundamental requirement for reproducibility across teams. Vertex AI supports custom containers for training, making this a direct and reliable method.
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.
✓
Lock dependency versions in a container image used for training
Why this is correct
Container images with fixed versions ensure environment reproducibility.
Related concept
Read the scenario before looking for a memorised answer.
✗
Share notebooks via Colab Enterprise with real-time editing
Why it's wrong here
Sharing notebooks does not enforce version control of data and dependencies.
✓
Version control datasets using DVC or Vertex AI ML Metadata
Why this is correct
Tracking dataset versions is critical for reproducibility.
Related concept
Read the scenario before looking for a memorised answer.
✗
Allow each team to use their own preferred environment
Why it's wrong here
Different environments lead to unreproducible results.
✗
Always use random seeds for all random operations
Why it's wrong here
Random seeds are not always applicable and can be inconsistent.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often think 'always use random seeds' is a safe blanket rule, but in practice, seeds must be explicitly set and logged per run, and some operations (e.g., certain GPU kernels) are inherently non-deterministic, making this option an oversimplification that is not a guaranteed action for reproducibility.
Detailed technical explanation
How to think about this question
Under the hood, container images are built with a Dockerfile that pins exact versions (e.g., `tensorflow==2.12.0`, `numpy==1.24.3`) and uses a base image with a specific OS version. When pushed to Artifact Registry and used in Vertex AI Training, the job pulls the same image hash, ensuring bit-for-bit identical environments. A subtle behavior: even with locked dependencies, hardware differences (e.g., GPU architecture) can cause floating-point non-determinism, so combining locked containers with seeded operations and deterministic algorithms (e.g., `torch.use_deterministic_algorithms(True)`) is the gold standard.
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.
Collaborating within and across teams to manage data and models — This question tests Collaborating within and across teams to manage data and models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Lock dependency versions in a container image used for training — Locking dependency versions in a container image ensures that the exact same software environment (e.g., Python packages, CUDA libraries, system tools) is used every time a training job runs. This eliminates variability from package updates or OS patches, which is a fundamental requirement for reproducibility across teams. Vertex AI supports custom containers for training, making this a direct and reliable method.
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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