The correct step is to remove the metric definition from the training job configuration. This resolves the XGBoost custom metric error in SageMaker because the built-in XGBoost algorithm does not support a custom metric like 'accuracy'—its native objective functions, such as 'binary:logistic' or 'reg:squarederror', simply do not compute accuracy. When you define a custom metric that XGBoost cannot evaluate, SageMaker fails the training job; removing that definition lets SageMaker fall back to supported default metrics like 'validation:rmse' or 'validation:error'. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of SageMaker’s integration with built-in algorithms and the distinction between custom metrics for hyperparameter tuning versus metrics that the algorithm itself can compute. A common trap is assuming any metric name can be passed, but XGBoost only evaluates metrics tied to its objective. Memory tip: if the metric isn’t in XGBoost’s native output, leave it out of the job config.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Network Topology
Refer to the exhibit. A data scientist ran a SageMaker training job using a built-in XGBoost algorithm. The job failed with the error shown. Which step should the data scientist take to fix the issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove the metric definition from the training job configuration
The error indicates that SageMaker's built-in XGBoost algorithm does not support a custom metric named 'accuracy' because XGBoost's built-in objective functions (e.g., 'binary:logistic', 'reg:squarederror') do not compute accuracy natively. Removing the metric definition from the training job configuration resolves the issue by allowing SageMaker to use the default metrics that XGBoost does support, such as 'validation:rmse' or 'validation:error'.
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.
Remove the metric definition from the training job configuration
Why this is correct
XGBoost will use its default metrics (rmse for regression) if not specified, avoiding the error.
Related concept
Read the scenario before looking for a memorised answer.
✗
Change the metric to 'validation:rmse'
Why it's wrong here
This would also work, but the question asks for a fix; removing the metric is simpler and correct.
✗
Use a different built-in algorithm that supports accuracy
Why it's wrong here
XGBoost can still be used; just need to specify a supported metric.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that any metric name can be used with built-in algorithms, when in fact the metric must be one of the predefined strings supported by the algorithm's container (e.g., 'validation:error' for XGBoost classification).
Detailed technical explanation
How to think about this question
SageMaker's built-in XGBoost uses the 'xgboost' container, which expects metric names that map to XGBoost's internal evaluation metrics (e.g., 'rmse', 'error', 'logloss'). When a custom metric like 'accuracy' is specified, the container cannot map it to an XGBoost evaluation function, causing the training job to fail. In practice, you can achieve accuracy monitoring by using 'validation:error' (which is 1 - accuracy) or by defining a custom metric in the training job's 'MetricDefinitions' parameter that computes accuracy from the output, but the simplest fix is to remove the unsupported metric definition entirely.
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 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
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Remove the metric definition from the training job configuration — The error indicates that SageMaker's built-in XGBoost algorithm does not support a custom metric named 'accuracy' because XGBoost's built-in objective functions (e.g., 'binary:logistic', 'reg:squarederror') do not compute accuracy natively. Removing the metric definition from the training job configuration resolves the issue by allowing SageMaker to use the default metrics that XGBoost does support, such as 'validation:rmse' or 'validation:error'.
What should I do if I get this MLS-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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Question Discussion
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