This MLA-C01 practice question tests your understanding of ml model development. 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.
Exhibit
Refer to the exhibit. A data scientist creates a SageMaker training job with the following configuration:
{
"AlgorithmSpecification": {
"TrainingImage": "382416733822.dkr.ecr.us-west-2.amazonaws.com/xgboost:1",
"TrainingInputMode": "File"
},
"InputDataConfig": [
{
"ChannelName": "train",
"DataSource": {
"S3DataSource": {
"S3DataType": "S3Prefix",
"S3Uri": "s3://my-bucket/train/",
"S3DataDistributionType": "FullyReplicated"
}
}
}
],
"HyperParameters": {
"objective": "reg:squarederror",
"num_round": "50",
"max_depth": "10"
},
"ResourceConfig": {
"InstanceType": "ml.m5.large",
"InstanceCount": 1,
"VolumeSizeInGB": 10
}
}
The training job completes successfully but the model performance is poor. What is a likely cause?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The max_depth hyperparameter is too high, leading to overfitting.
A max_depth of 10 is high for many datasets, often leading to overfitting. Overfitting results in poor generalization. The other options are less likely: num_round=50 is moderate, instance type is not directly related to model performance, and data shuffling is not specified but the primary issue is hyperparameter choice.
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.
✗
The training data is not shuffled.
Why it's wrong here
While shuffling can affect training, the poor performance is more likely due to overfitting from a high max_depth.
✗
The instance type is too small for the dataset.
Why it's wrong here
The job completed successfully, so the instance size is sufficient; model performance is separate.
✗
The number of rounds (num_round) is too high.
Why it's wrong here
50 rounds is a typical value for XGBoost; it is not excessively high.
✓
The max_depth hyperparameter is too high, leading to overfitting.
Why this is correct
A max_depth of 10 can cause overfitting, especially on smaller datasets, resulting in poor generalization.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The max_depth hyperparameter is too high, leading to overfitting. — A max_depth of 10 is high for many datasets, often leading to overfitting. Overfitting results in poor generalization. The other options are less likely: num_round=50 is moderate, instance type is not directly related to model performance, and data shuffling is not specified but the primary issue is hyperparameter choice.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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