- A
Install MLflow on the SageMaker notebook instance only
Why wrong: This would track only exploratory work, not the training jobs.
- B
Use the SageMaker Experiments integration with MLflow
Why wrong: SageMaker Experiments is a separate tracking service; integration with MLflow is not native.
- C
Set the MLFLOW_TRACKING_URI environment variable in the training job and use the mlflow library in the training script
This is the standard way to use MLflow with SageMaker; it allows logging metrics, parameters, and artifacts.
- D
Use SageMaker Processing to run MLflow after training
Why wrong: Post-processing is not real-time tracking; MLflow should be used during training.
Question 103 of 1,000
MLflow Tracking with SageMaker
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.
An ML engineer wants to use MLflow on SageMaker to track experiments and log metrics. They have set up MLflow on an EC2 instance. How can they best integrate MLflow tracking with SageMaker training jobs?
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
✓
Set the MLFLOW_TRACKING_URI environment variable in the training job and use the mlflow library in the training script
Option C is correct because the ML engineer can set the `MLFLOW_TRACKING_URI` environment variable in the SageMaker training job definition and use the `mlflow` library inside the training script to log parameters, metrics, and artifacts directly to the MLflow tracking server running on the EC2 instance. This approach allows the training job to communicate with the external MLflow server over HTTP/HTTPS without requiring any additional SageMaker integrations.
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.
- ✗
Install MLflow on the SageMaker notebook instance only
Why it's wrong here
This would track only exploratory work, not the training jobs.
- ✗
Use the SageMaker Experiments integration with MLflow
Why it's wrong here
SageMaker Experiments is a separate tracking service; integration with MLflow is not native.
- ✓
Set the MLFLOW_TRACKING_URI environment variable in the training job and use the mlflow library in the training script
Why this is correct
This is the standard way to use MLflow with SageMaker; it allows logging metrics, parameters, and artifacts.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker Processing to run MLflow after training
Why it's wrong here
Post-processing is not real-time tracking; MLflow should be used during training.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that SageMaker Experiments is required for tracking with MLflow, but the correct approach is to directly configure the MLflow tracking URI and use the mlflow library in the training script, as SageMaker does not natively block outbound HTTP connections to an external MLflow server.
Detailed technical explanation
How to think about this question
Under the hood, the `MLFLOW_TRACKING_URI` environment variable tells the `mlflow` client library where to send tracking data (e.g., `http://<ec2-public-dns>:5000`). The training script must import `mlflow` and call `mlflow.set_tracking_uri()` or rely on the environment variable, then use `mlflow.log_param()`, `mlflow.log_metric()`, etc. A subtle behavior is that the EC2 instance's security group must allow inbound traffic on port 5000 from the SageMaker training job's security group or IP range, and the training job must have outbound internet access if the EC2 instance is in a public subnet.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
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: Set the MLFLOW_TRACKING_URI environment variable in the training job and use the mlflow library in the training script — Option C is correct because the ML engineer can set the `MLFLOW_TRACKING_URI` environment variable in the SageMaker training job definition and use the `mlflow` library inside the training script to log parameters, metrics, and artifacts directly to the MLflow tracking server running on the EC2 instance. This approach allows the training job to communicate with the external MLflow server over HTTP/HTTPS without requiring any additional SageMaker integrations.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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