The answer is to use SageMaker Batch Transform with separate jobs for each model. This approach is most efficient for weekly batch inference because Batch Transform automatically provisions compute resources for each job and terminates them upon completion, eliminating the cost of idle infrastructure while the independent jobs allow each model to scale precisely to its own data volume without interference. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of when to choose ephemeral batch processing over persistent endpoints; a common trap is selecting multi-model endpoints or real-time inference, which incur ongoing costs and management overhead unsuitable for infrequent workloads. Remember the key distinction: batch jobs are like renting a car only for the trip you need, while endpoints are like owning a car you must maintain daily. A useful memory tip is “Batch for batch, endpoints for real-time”—if the inference is scheduled and not latency-sensitive, Batch Transform is the leanest choice.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. 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.
A team has a large number of models that need to be deployed for batch inference weekly. They want to minimize cost and management overhead. Which approach is MOST efficient?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
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
✓
Use SageMaker Batch Transform with separate jobs for each model.
SageMaker Batch Transform is the most efficient approach for weekly batch inference because it automatically provisions and terminates compute resources for each job, minimizing cost and management overhead. Running separate jobs for each model allows independent scaling and avoids the complexity of managing persistent endpoints or multi-model hosting for batch workloads.
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.
✗
Use SageMaker Pipelines to run inference as part of the pipeline.
Why it's wrong here
Pipelines are for orchestration, not primarily for batch inference.
✓
Use SageMaker Batch Transform with separate jobs for each model.
Why this is correct
Batch Transform jobs are ephemeral and cost-effective for batch workloads.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create a single SageMaker endpoint for all models and update the model periodically.
Why it's wrong here
A single endpoint can only host one model at a time, requiring frequent updates.
✗
Deploy each model to a separate SageMaker endpoint and delete after use.
Why it's wrong here
Managing many endpoints increases overhead and cost.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between batch and real-time inference, where candidates mistakenly choose persistent endpoints (Option C or D) for batch workloads, overlooking that Batch Transform is purpose-built for cost-efficient, ephemeral batch processing.
Detailed technical explanation
How to think about this question
SageMaker Batch Transform uses a managed cluster of ML compute instances that are automatically launched, run the inference job, and then terminated, charging only for the duration of the job. This is ideal for weekly batch workloads where models are independent, as each job can use a different instance type or size without manual intervention. In contrast, persistent endpoints incur hourly costs even when idle, and multi-model endpoints require careful resource allocation to avoid contention.
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.
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SageMaker Batch Transform with separate jobs for each model. — SageMaker Batch Transform is the most efficient approach for weekly batch inference because it automatically provisions and terminates compute resources for each job, minimizing cost and management overhead. Running separate jobs for each model allows independent scaling and avoids the complexity of managing persistent endpoints or multi-model hosting for batch workloads.
What should I do if I get this MLA-C01 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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Your company uses SageMaker batch transform to process a large dataset (5 TB) of customer transactions every night. The batch transform job uses a single ml.c5.4xlarge instance and takes about 6 hours to complete. However, the job recently started failing with an error message: 'Timed out waiting for transformation to complete. The maximum job duration is 3600 seconds.' You check the input data and notice that one of the input files is a single large JSON file of 50 GB, while the rest are smaller files. The job is configured with a batch strategy of 'MultiRecord' and a maximum payload size of 6 MB. What is the most likely cause of the timeout and which fix should you apply?
medium
A.Set the batch strategy to 'SingleRecord' so that each record is processed individually.
✓ B.Split the large JSON file into smaller files (e.g., 100 MB each) before feeding to the batch transform job.
C.Increase the job timeout to 7200 seconds.
D.Increase the number of instances to 5 in the batch transform job.
Why B: The batch transform job is timing out because the single 50 GB JSON file cannot be processed within the default 3600-second (1-hour) timeout. With a 'MultiRecord' batch strategy and a 6 MB maximum payload size, SageMaker must split the large file into many small batches, but the job still tries to read the entire file sequentially, causing excessive processing time. Splitting the large file into smaller files (e.g., 100 MB each) allows SageMaker to parallelize and complete the transform within the timeout.
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Question Discussion
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