The answer is missing GPU acceleration for inference. This is the most likely cause because GPT-2 is a transformer-based neural network that relies on massive parallel matrix multiplications, which CPUs execute sequentially and inefficiently, while GPUs leverage thousands of cores and tensor cores to process these operations simultaneously. Even a small 500MB model on a VM.Standard.E3.Flex shape with only 2 OCPU will struggle with the attention mechanism and feed-forward layers, producing the classic symptom of 10-second latency for a short 50-token prompt. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your understanding that model size is not the bottleneck—architecture is; a common trap is assuming a small model runs fine on CPU, but transformer inference demands GPU parallelism. Remember the memory tip: “Transformers need tensor cores, not just CPU cores.”
1Z0-1127 Deploying and Managing Generative AI on OCI Practice Question
This 1Z0-1127 practice question tests your understanding of deploying and managing generative ai on oci. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Your team has deployed a fine-tuned GPT-2 model on OCI Model Deployment for a simple text generation API. The model performs text completion for short prompts (e.g., 50 tokens). The endpoint is working but response times are over 10 seconds for these short prompts. The model size is approximately 500MB and you used a VM.Standard.E3.Flex shape (2 OCPU, 16GB RAM). The deployment is in a single replica with no autoscaling. You have verified that the network latency is minimal (<5ms). The model was trained in OCI Data Science using a GPU shape, but during deployment you selected a CPU shape to reduce cost. The model is a transformer-based neural network. You've also confirmed that the deployment is healthy and there are no errors in the logs. The memory usage is within limits. What is the most likely cause of the high latency?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Missing GPU acceleration for inference
Option D is correct because GPT-2 is a transformer-based neural network that relies heavily on matrix multiplications, which are far more efficiently executed on GPUs due to their parallel architecture. Even though the model is only 500MB, CPU inference for transformer models is notoriously slow because CPUs process sequential operations, while GPUs can parallelize the attention mechanism and feed-forward layers. The 10-second latency for a 50-token prompt is a classic symptom of missing GPU acceleration, as the CPU shape (2 OCPU) lacks the specialized tensor cores needed for fast transformer inference.
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.
✗
High network latency between the client and the model endpoint
Why it's wrong here
Network latency is verified minimal (<5ms), so not the cause.
✗
Model is too large for the VM.Standard.E3.Flex shape
Why it's wrong here
The model size (500MB) is well within the 16GB RAM of the shape.
✗
Insufficient CPU resources for the model size
Why it's wrong here
CPU resources are adequate (2 OCPU) but the bottleneck is the lack of GPU for parallel computation.
✓
Missing GPU acceleration for inference
Why this is correct
GPU acceleration is essential for fast inference on neural network models like GPT-2.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates might assume a 500MB model is 'small enough' for CPU inference, overlooking that transformer architecture—not model size—is the primary driver of latency, and that GPU acceleration is essential even for moderately sized transformer models.
Detailed technical explanation
How to think about this question
Transformer models like GPT-2 use self-attention mechanisms that involve large matrix multiplications (e.g., Q, K, V projections) which are inherently parallelizable. GPUs contain thousands of CUDA cores optimized for such operations, achieving orders-of-magnitude speedup over CPUs. In OCI Model Deployment, selecting a GPU shape (e.g., VM.GPU.A10.1) would enable the model to leverage NVIDIA CUDA libraries and cuBLAS for accelerated inference, reducing response times from seconds to milliseconds for short prompts.
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 practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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.
Deploying and Managing Generative AI on OCI — This question tests Deploying and Managing Generative AI on OCI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Missing GPU acceleration for inference — Option D is correct because GPT-2 is a transformer-based neural network that relies heavily on matrix multiplications, which are far more efficiently executed on GPUs due to their parallel architecture. Even though the model is only 500MB, CPU inference for transformer models is notoriously slow because CPUs process sequential operations, while GPUs can parallelize the attention mechanism and feed-forward layers. The 10-second latency for a 50-token prompt is a classic symptom of missing GPU acceleration, as the CPU shape (2 OCPU) lacks the specialized tensor cores needed for fast transformer inference.
What should I do if I get this 1Z0-1127 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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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