- A
Built-in model monitoring
Vertex AI Endpoints integrates with Model Monitoring; Cloud Run requires custom implementation.
- B
Complexity of model containerization
Why wrong: Both require containerization; the complexity is comparable.
- C
Cost per request
Why wrong: Both services have similar pricing models (pay per request + compute time); not a strong differentiator.
- D
GPU support
Vertex AI Endpoints offers native GPU support; Cloud Run has limited GPU availability (preview).
- E
Automatic scaling to zero
Cloud Run scales to zero by default; Vertex AI Endpoints requires minReplicaCount=0 and may still incur costs.
Question 37 of 1,000
mediumMultiple SelectObjective-mapped
Choose Between Vertex AI Endpoints and Cloud Run
This PMLE practice question tests your understanding of pmle exam topics. 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 factors should be considered when choosing between using Vertex AI Endpoints and Cloud Run for model serving? (Choose three.)
Quick Answer
The answer is automatic scaling to zero, native GPU support, and built-in model monitoring. These three factors are the key differentiators when choosing between Vertex AI Endpoints and Cloud Run for model serving because Vertex AI Endpoints is purpose-built for ML workloads, offering native GPU acceleration and integrated model monitoring for drift detection, while Cloud Run, as a serverless compute platform, excels at scaling to zero when idle—a feature Vertex AI Endpoints does not easily achieve. On the Google Professional Machine Learning Engineer exam, this question tests your ability to match deployment infrastructure to operational requirements, often appearing as a scenario where you must prioritize cost efficiency versus performance monitoring. A common trap is assuming both services have identical scaling behaviors or cost structures, but the exam emphasizes that Cloud Run’s scale-to-zero is inherent, whereas Vertex AI Endpoints requires manual configuration for similar idle savings. Remember the mnemonic “GPU, Monitor, Zero” to recall the three decisive factors: GPU support, monitoring capabilities, and zero-scaling behavior.
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
✓
Built-in model monitoring
The key differentiators between Vertex AI Endpoints and Cloud Run for model serving are built-in model monitoring, GPU support, and automatic scaling to zero. Vertex AI Endpoints provides built-in model monitoring, while Cloud Run does not offer this natively. GPU support is a strong differentiator: Vertex AI Endpoints natively supports GPUs, whereas Cloud Run has very limited GPU support, often making it unsuitable for GPU-dependent models. Automatic scaling to zero is a feature of Cloud Run, which can scale down to zero instances when not in use, reducing costs; Vertex AI Endpoints typically requires at least one instance, so it does not scale to zero as easily. In contrast, the complexity of model containerization and cost per request are less differentiating: both services require similar containerization effort and have comparable per-request pricing models, though cost specifics depend on usage patterns.
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.
- ✓
Built-in model monitoring
Why this is correct
Vertex AI Endpoints integrates with Model Monitoring; Cloud Run requires custom implementation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Complexity of model containerization
Why it's wrong here
Both require containerization; the complexity is comparable.
- ✗
Cost per request
Why it's wrong here
Both services have similar pricing models (pay per request + compute time); not a strong differentiator.
- ✓
GPU support
Why this is correct
Vertex AI Endpoints offers native GPU support; Cloud Run has limited GPU availability (preview).
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Automatic scaling to zero
Why this is correct
Cloud Run scales to zero by default; Vertex AI Endpoints requires minReplicaCount=0 and may still incur costs.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Similar concept trap
Both services have similar pricing models (pay per request + compute time); not a strong differentiator.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 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
Got this wrong? Here's your next step.
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Related practice questions
Related PMLE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Automating and Orchestrating ML Pipelines practice questions
Practise PMLE questions linked to Automating and Orchestrating ML Pipelines.
Collaborating Within and Across Teams to Manage Data and Models practice questions
Practise PMLE questions linked to Collaborating Within and Across Teams to Manage Data and Models.
Serving and Scaling Models practice questions
Practise PMLE questions linked to Serving and Scaling Models.
Monitoring ML Solutions practice questions
Practise PMLE questions linked to Monitoring ML Solutions.
Architecting Low-Code ML Solutions practice questions
Practise PMLE questions linked to Architecting Low-Code ML Solutions.
Scaling Prototypes into ML Models practice questions
Practise PMLE questions linked to Scaling Prototypes into ML Models.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
Solving business challenges with ML practice questions
Practise PMLE questions linked to Solving business challenges with ML.
PMLE fundamentals practice questions
Practise PMLE questions linked to PMLE fundamentals.
PMLE scenario practice questions
Practise PMLE questions linked to PMLE scenario.
PMLE troubleshooting practice questions
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Start a free PMLE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this PMLE question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Built-in model monitoring — The key differentiators between Vertex AI Endpoints and Cloud Run for model serving are built-in model monitoring, GPU support, and automatic scaling to zero. Vertex AI Endpoints provides built-in model monitoring, while Cloud Run does not offer this natively. GPU support is a strong differentiator: Vertex AI Endpoints natively supports GPUs, whereas Cloud Run has very limited GPU support, often making it unsuitable for GPU-dependent models. Automatic scaling to zero is a feature of Cloud Run, which can scale down to zero instances when not in use, reducing costs; Vertex AI Endpoints typically requires at least one instance, so it does not scale to zero as easily. In contrast, the complexity of model containerization and cost per request are less differentiating: both services require similar containerization effort and have comparable per-request pricing models, though cost specifics depend on usage patterns.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More PMLE practice questions
- A travel booking company has a real-time recommendation system that suggests hotels and flights to users. The model is s…
- A global retail company uses Vertex AI Recommendations to provide product recommendations on their website. They have a…
- Your team is developing a machine learning model for real-time fraud detection. The training pipeline runs on Vertex AI…
- A healthcare organization is building a machine learning model to predict patient readmission risk. They have sensitive…
- You are an ML engineer at a global e-commerce company. Your team has developed a deep learning model for product recomme…
- A financial services company uses Vertex AI AutoML Tables to build a credit risk model. The dataset contains 500,000 row…
Last reviewed: Jun 24, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.