This MLA-C01 practice question tests your understanding of ml model development. 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.
A company deploys a real-time inference endpoint on SageMaker for a customer-facing application. Traffic patterns are unpredictable and sometimes spike. The endpoint must scale automatically to handle load while minimizing cost. Which approach should the company take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure a target tracking scaling policy on the endpoint using CloudWatch metrics.
Option D is correct because a target tracking scaling policy with a metric like average latency or request count scales automatically based on demand. Option A is wrong because using multiple models does not address scaling. Option B is wrong because increasing instance type without auto-scaling leads to over-provisioning. Option C is wrong because batch transform is for asynchronous, not real-time.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Switch to batch transform for all inference requests.
Why it's wrong here
Batch transform is for offline processing, not real-time.
✗
Use a larger instance type to handle peak traffic.
Why it's wrong here
Over-provisioning costs more and still may not handle sudden spikes beyond capacity.
✓
Configure a target tracking scaling policy on the endpoint using CloudWatch metrics.
Why this is correct
Target tracking scales instances in/out based on a metric threshold, matching demand.
Related concept
Static NAT maps one inside address to one outside address.
✗
Deploy multiple models behind an Application Load Balancer.
Why it's wrong here
ALB distributes traffic but does not provide the same auto-scaling integration for SageMaker endpoints.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
ML Model Development — This question tests ML Model Development — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Configure a target tracking scaling policy on the endpoint using CloudWatch metrics. — Option D is correct because a target tracking scaling policy with a metric like average latency or request count scales automatically based on demand. Option A is wrong because using multiple models does not address scaling. Option B is wrong because increasing instance type without auto-scaling leads to over-provisioning. Option C is wrong because batch transform is for asynchronous, not real-time.
What should I do if I get this MLA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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