This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.
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?
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
}
}
A
The training data is not shuffled.
Why wrong: While shuffling can affect training, the poor performance is more likely due to overfitting from a high max_depth.
B
The instance type is too small for the dataset.
Why wrong: The job completed successfully, so the instance size is sufficient; model performance is separate.
C
The number of rounds (num_round) is too high.
Why wrong: 50 rounds is a typical value for XGBoost; it is not excessively high.
D
The max_depth hyperparameter is too high, leading to overfitting.
A max_depth of 10 can cause overfitting, especially on smaller datasets, resulting in poor generalization.
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.
Option D is correct because a max_depth that is too high causes the model to learn overly specific patterns from the training data, including noise, leading to overfitting. This results in poor generalization to unseen data, even though the training job completes successfully. Overfitting is a common cause of high training accuracy but low validation/test performance.
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
A common trap is confusing overfitting (high max_depth) with underfitting (low max_depth) or attributing poor performance to instance type or data issues, rather than recognizing that successful training with poor validation indicates overfitting.
Detailed technical explanation
How to think about this question
In tree-based models like XGBoost or LightGBM, max_depth controls the depth of each decision tree. A high max_depth allows trees to split many times, capturing interactions that may be spurious. Under the hood, this increases model complexity and variance, often leading to overfitting, especially with small datasets. In practice, cross-validation and monitoring training vs. validation loss are essential to detect overfitting early.
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.
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. — Option D is correct because a max_depth that is too high causes the model to learn overly specific patterns from the training data, including noise, leading to overfitting. This results in poor generalization to unseen data, even though the training job completes successfully. Overfitting is a common cause of high training accuracy but low validation/test performance.
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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Question Discussion
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