This PDE practice question tests your understanding of operationalizing machine learning 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 THREE Google Cloud services are typically used together in a production ML pipeline?
Cloud Storage
For storing training data, model artifacts, etc.
Cloud Functions
Why wrong: Not a core ML pipeline component.
Vertex AI Training
For model training at scale.
Vertex AI Prediction
For deploying and serving models.
BigQuery
Why wrong: Optional; used for analytics but not always in ML pipeline.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Cloud Storage
Cloud Storage is correct because it serves as the central artifact repository in a production ML pipeline on Google Cloud. It stores training data, model artifacts, and prediction inputs/outputs, enabling seamless integration with Vertex AI Training for model training and Vertex AI Prediction for serving. Without Cloud Storage, there is no durable, scalable, and cost-effective way to manage the large datasets and model binaries required for production ML workflows.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Cloud Storage
Why this is correct
For storing training data, model artifacts, etc.
Related concept
Read the scenario before looking for a memorised answer.
Cloud Functions
Why it's wrong here
Not a core ML pipeline component.
Vertex AI Training
Why this is correct
For model training at scale.
Related concept
Read the scenario before looking for a memorised answer.
Vertex AI Prediction
Why this is correct
For deploying and serving models.
Related concept
Read the scenario before looking for a memorised answer.
BigQuery
Why it's wrong here
Optional; used for analytics but not always in ML pipeline.
Common exam traps
The trap here is that candidates confuse 'services used in an ML pipeline' with 'services that can be used somewhere in ML' — Cloud Functions and BigQuery are often used in ML workflows (e.g., triggering retraining or storing features), but they are not the three core services that are typically used together in a production ML pipeline for training, storing artifacts, and serving predictions.
Detailed technical explanation
In a production ML pipeline on Google Cloud, Cloud Storage acts as the unified object store for all pipeline stages: raw data (e.g., CSV/Parquet files), processed features (e.g., TFRecords), trained model artifacts (e.g., SavedModel.pb), and prediction results. Vertex AI Training and Prediction both read from and write to Cloud Storage buckets via gsutil or the Cloud Storage client libraries, using the same URI scheme (gs://bucket/path). A real-world scenario is a churn prediction pipeline where Cloud Storage holds daily feature snapshots, the trained XGBoost model, and batch prediction outputs, all accessed by Vertex AI components without needing to manage persistent disks or shared file systems.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
The correct answer is: Cloud Storage — Cloud Storage is correct because it serves as the central artifact repository in a production ML pipeline on Google Cloud. It stores training data, model artifacts, and prediction inputs/outputs, enabling seamless integration with Vertex AI Training for model training and Vertex AI Prediction for serving. Without Cloud Storage, there is no durable, scalable, and cost-effective way to manage the large datasets and model binaries required for production ML workflows.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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