hardMultiple SelectObjective-mapped
AIF-C01 Practice Question: A machine learning engineer is using Amazon…
A machine learning engineer is using Amazon SageMaker to deploy a real-time inference endpoint for a classification model. The model must provide low-latency predictions and handle variable traffic. Which THREE actions should the engineer take? (Select THREE.)
⚠ Common exam trap
Many candidates confuse throughput optimization (larger instances) with elasticity (auto scaling), or mistakenly think batch transform can serve real-time traffic, when in fact batch jobs have no endpoint and cannot provide sub-second latency.
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
✓
Enable data capture to log input and output payloads
Enabling data capture in SageMaker allows the engineer to log input and output payloads for all predictions made by the real-time endpoint. This is essential for monitoring, auditing, and debugging model performance without impacting latency, as the capture is asynchronous and stored in Amazon S3.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable data capture to log input and output payloads
Why this is correct
Data capture is important for monitoring, auditing, and debugging predictions.
- ✗
Use a larger instance type to maximize throughput
Why it's wrong here
Larger instances may increase latency due to processing overhead; smaller instances are better for low latency.
- ✓
Choose an instance type optimized for inference, such as Inf1
Why this is correct
Inference-optimized instances are designed for low-latency predictions.
- ✗
Deploy the model as a batch transform job instead of a real-time endpoint
Why it's wrong here
Batch transform is for offline predictions, not real-time low-latency inference.
- ✓
Configure auto scaling to add or remove instances based on traffic
Why this is correct
Auto scaling ensures the endpoint can handle variable traffic without manual intervention.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.