The correct choice is a managed online endpoint with a GPU VM, because it provisions a dedicated GPU instance that remains always active, ensuring the model is available for real-time inference without cold-start delays. This option directly addresses cost optimization for real-time GPU inference in Azure ML by allowing you to select a lower-cost GPU SKU, such as the NCas_v4 series, while maintaining constant availability through a fixed minimum instance count. On the AI-102 exam, this scenario tests your understanding of balancing cost and latency for generative AI deployments—a common trap is choosing serverless endpoints or batch inference, which introduce startup latency or lack always-on capability. Remember the memory tip: “Always-on needs a fixed node; serverless is for sporadic loads.”
AI-102 Implement generative AI solutions Practice Question
This AI-102 practice question tests your understanding of implement generative ai solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
You are deploying a generative AI model using Azure Machine Learning. The model is a large language model that requires GPU compute. You need to minimize costs while ensuring the model is always available for inference. Which compute option should you choose?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "always"
Why it matters: Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Managed online endpoint with a GPU VM
A managed online endpoint with a GPU VM is the correct choice because it provisions a dedicated GPU instance that remains always active, ensuring the model is available for real-time inference at any time. This option balances cost and availability by allowing you to choose a lower-cost GPU SKU (e.g., NCas_v4) while avoiding the cold-start latency of serverless or batch options. The managed endpoint also handles auto-scaling and load balancing, but for constant availability, a fixed minimum instance count is required.
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.
✓
Managed online endpoint with a GPU VM
Why this is correct
Managed online endpoints provide real-time inference with GPU, and autoscaling can help cost while keeping availability.
Clue confirmation
The clue words "always", "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Batch endpoint with GPU
Why it's wrong here
Batch endpoints are for offline processing, not real-time always-on.
✗
Serverless GPU compute
Why it's wrong here
Serverless may have cold start delays and is not always immediately available.
✗
CPU-based inference
Why it's wrong here
CPU is too slow for large language models.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the distinction between 'always available' (requiring a persistent compute instance) and 'cost-optimized' (allowing scale-to-zero), leading candidates to mistakenly choose serverless GPU compute because it sounds cheaper, but it fails the availability requirement.
Detailed technical explanation
How to think about this question
Under the hood, managed online endpoints use Azure Kubernetes Service (AKS) or container instances to host the model, with GPU passthrough via NVIDIA MIG or GPU partitioning. The 'always available' requirement means you must set a minimum replica count (e.g., min_replicas=1) and disable scale-to-zero, which incurs continuous GPU compute costs but ensures sub-second response times. In a real-world scenario, a customer deploying GPT-2 for a chatbot would choose a managed endpoint with a single T4 GPU to keep costs under $1/hour while maintaining 24/7 availability.
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 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Managed online endpoint with a GPU VM — A managed online endpoint with a GPU VM is the correct choice because it provisions a dedicated GPU instance that remains always active, ensuring the model is available for real-time inference at any time. This option balances cost and availability by allowing you to choose a lower-cost GPU SKU (e.g., NCas_v4) while avoiding the cold-start latency of serverless or batch options. The managed endpoint also handles auto-scaling and load balancing, but for constant availability, a fixed minimum instance count is required.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "always", "minimum / minimize". Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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