Quick Answer
The answer is CreateTrainingJob, as this is the specific SageMaker API operation designed to launch a training job. It works by packaging all essential parameters—the custom TensorFlow script or algorithm container, the S3 data paths for input, the instance type and count for compute resources, and the output location for model artifacts—into a single request that SageMaker’s managed infrastructure then executes. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding of the core workflow for model training, often appearing in scenario-based questions where you must distinguish CreateTrainingJob from related operations like CreateHyperParameterTuningJob or CreateProcessingJob. A common trap is confusing it with CreateTrainingJob’s sibling, CreateAutoMLJob, which handles automated model selection rather than custom script training. To remember, think of the mnemonic “CTJ: Code, Train, Job”—you provide the code (script), specify the training resources, and launch a job.
This MLS-C01 practice question tests your understanding of modeling. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 wants to use Amazon SageMaker to train a deep learning model using a custom TensorFlow script. The data is stored in an S3 bucket. Which SageMaker API operation should be used to launch the training job?
CreateHyperParameterTuningJob
Why wrong: That is for hyperparameter tuning, not a single training job.
CreateEndpoint
Why wrong: CreateEndpoint is for real-time inference.
CreateTransformJob
Why wrong: CreateTransformJob is for batch inference.
CreateTrainingJob
CreateTrainingJob starts a training job.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
CreateTrainingJob
The correct API operation to launch a training job in Amazon SageMaker is CreateTrainingJob. This operation specifies the training algorithm (or custom script), resource configuration (instance type and count), input data configuration (pointing to S3), and output location for the model artifacts. It directly initiates the training process on SageMaker-managed infrastructure.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
CreateHyperParameterTuningJob
Why it's wrong here
That is for hyperparameter tuning, not a single training job.
CreateEndpoint
Why it's wrong here
CreateEndpoint is for real-time inference.
CreateTransformJob
Why it's wrong here
CreateTransformJob is for batch inference.
CreateTrainingJob
Why this is correct
CreateTrainingJob starts a training job.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Cisco often tests the distinction between training, tuning, inference, and batch transform operations, so candidates mistakenly choose CreateHyperParameterTuningJob when the question only asks for a single training job, or choose CreateEndpoint when they confuse training with deployment.
Detailed technical explanation
Under the hood, CreateTrainingJob submits a request to the SageMaker control plane, which provisions the specified EC2 instances, copies the training code and data from S3, runs the training script, and saves the resulting model artifacts back to S3. A subtle behavior is that the training job's input data can be configured with ShuffleConfig for random shuffling or with RecordIO-wrapped protobuf format for optimized pipe mode streaming, which can significantly reduce training start times.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: CreateTrainingJob — The correct API operation to launch a training job in Amazon SageMaker is CreateTrainingJob. This operation specifies the training algorithm (or custom script), resource configuration (instance type and count), input data configuration (pointing to S3), and output location for the model artifacts. It directly initiates the training process on SageMaker-managed infrastructure.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This MLS-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 MLS-C01 exam.
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