PMLE Scaling Prototypes into ML Models Practice Question
You need to run a custom training job on Vertex AI using a pre-built container for scikit-learn. Which container image should you specify?
⚠ Common exam trap
PMLE often tests the ability to match the correct pre-built container image to the framework, causing candidates to confuse scikit-learn with XGBoost or TensorFlow images.
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
✓
us-docker.pkg.dev/vertex-ai/training/scikit-learn-cpu.0.23-0
For a scikit-learn custom training job on Vertex AI, you must specify a pre-built container image that includes scikit-learn. The correct image is us-docker.pkg.dev/vertex-ai/training/scikit-learn-cpu.0.23-0, which is the official Vertex AI pre-built training container for scikit-learn version 0.23 on CPU. Other images correspond to different frameworks (PyTorch, XGBoost, TensorFlow).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.1-9
Why it's wrong here
The pytorch-gpu image ships PyTorch libraries, so scikit-learn training code cannot import sklearn within it. It is tempting because GPU-accelerated pre-built containers suit deep learning workloads, and this image would be correct for training a PyTorch model requiring GPU acceleration.
- ✓
us-docker.pkg.dev/vertex-ai/training/scikit-learn-cpu.0.23-0
Why this is correct
Vertex AI provides pre-built training containers hosted in its Artifact Registry; the scikit-learn CPU image at that path bundles the framework and Python dependencies needed to run custom scikit-learn training code. Specifying it satisfies the stem's requirement to use a pre-built container rather than building your own.
- ✗
us-docker.pkg.dev/vertex-ai/training/xgboost-cpu.1-3
Why it's wrong here
The xgboost-cpu image provides XGBoost, not scikit-learn, so scikit-learn training code would fail at import. It is tempting because XGBoost is a scikit-learn-compatible gradient boosting library, and this image would be correct when training an XGBoost model on CPU.
- ✗
us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-6
Why it's wrong here
The tf-cpu image bundles TensorFlow, not scikit-learn, so the job's scikit-learn training code would fail to import its dependencies. It is tempting because TensorFlow is a common Vertex AI pre-built container, and it would be the correct choice when training a TensorFlow model on CPU.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.