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This page has 8 Troubleshooting Scenario Questions scenario questions for the MLS-C01 exam, each with detailed explanations and wrong-answer analysis.

How should I approach MLS-C01 scenario questions?

Read the full scenario before looking at the answer options. Identify the constraint or requirement in the scenario, then eliminate options that are generally true but wrong for this specific case. Scenario questions reward careful reading over pattern matching.

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Troubleshooting Scenario Questions

Practise AWS Certified Machine Learning Specialty MLS-C01 practice questions — original exam-style scenarios covering every exam domain, with detailed explanations, wrong-answer analysis, and common exam traps.

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scenario questions

MLS-C01

exam code

Amazon Web Services

vendor

Scenario guide

How to approach troubleshooting scenario questions

These questions describe a network symptom and ask you to identify the root cause or the correct fix. They appear across all certification exams and reward systematic thinking over memorisation. The best candidates follow a consistent troubleshooting framework even under time pressure.

Quick answer

Troubleshooting Scenario Questions questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Practice scenarios

Question 1easymulti select

Full question →

During EDA, a data scientist notices that a numeric feature 'age' has values ranging from 0 to 150, but expects adult ages between 18-100. Which TWO steps should the scientist take to investigate?

A
Remove all rows with age > 100
Why wrong: Incorrect because removing rows without understanding context may discard valid data.
B
Compute summary statistics (min, max, percentiles)
Correct because it helps identify the range and potential outliers.
C
Apply log transformation to normalize the distribution
Why wrong: Incorrect because log transformation does not help in identifying outliers; it is used for skewed distributions.
D
Impute age values outside 18-100 with the mean
Why wrong: Incorrect because imputing with mean would distort the distribution and is not appropriate for investigating outliers.
E
Create a box plot to visualize outliers
Correct because a box plot visualizes the distribution and clearly shows outliers.

Full breakdown with real-world context →

Question 2mediummultiple choice

Full question →

Refer to the exhibit. A data scientist is unable to run an Amazon Athena query on data in `my-bucket`. The IAM policy shown is attached to the user. What is the most likely reason for the failure?

Exhibit

Refer to the exhibit.
```
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:ListBucket"
      ],
      "Resource": [
        "arn:aws:s3:::my-bucket",
        "arn:aws:s3:::my-bucket/*"
      ]
    }
  ]
}
```

A
The ListBucket action is not granted.
Why wrong: ListBucket is included.
B
Athena needs s3:PutObject permission to write results.
Athena writes output to S3.
C
The data is encrypted with SSE-C.
Why wrong: No encryption indication.
D
The bucket does not exist.
Why wrong: The policy references the bucket.

Full breakdown with real-world context →

Question 3hardmulti select

Full question →

A data scientist is performing EDA on a dataset stored in Amazon S3 using Amazon Athena. The dataset is partitioned by date, and each partition contains CSV files. The data scientist notices that some queries return zero rows for partitions that should have data. Which THREE steps should the data scientist take to troubleshoot? (Choose 3.)

A
Verify that the CSV files exist in the S3 bucket for the specific partition.
Files may have been moved or deleted.
B
Run MSCK REPAIR TABLE to add new partitions to the Glue Data Catalog.
Partitions may not have been discovered by the crawler.
C
Convert the CSV files to Parquet format.
Why wrong: Format conversion is not a troubleshooting step for missing data.
D
Check the data types of the columns used in the query's WHERE clause.
Type mismatches can cause the filter to exclude all rows.
E
Re-run the query with a LIMIT clause to force partition discovery.
Why wrong: LIMIT does not trigger partition discovery.

Full breakdown with real-world context →

Question 4mediummultiple choice

Full question →

A data scientist is using Amazon SageMaker to train a natural language processing model using a custom Docker container. The training script reads data from an S3 bucket and writes checkpoints to an S3 bucket. The training job is failing with the error 'Unable to write to checkpoint path: s3://my-bucket/checkpoints/'. The IAM role associated with the training job has the following policy: {'Effect': 'Allow', 'Action': 's3:PutObject', 'Resource': 'arn:aws:s3:::my-bucket/checkpoints/*'}. The bucket 'my-bucket' exists and the prefix 'checkpoints/' is empty. What is the most likely cause of the failure?

A
The IAM role is missing the s3:ListBucket permission
SageMaker needs ListBucket to access the bucket.
B
The IAM role does not have s3:PutObject permission
Why wrong: PutObject is allowed.
C
The S3 bucket does not exist
Why wrong: The bucket exists.
D
The checkpoint prefix already contains objects
Why wrong: The prefix is empty.

Full breakdown with real-world context →

Question 5hardmultiple choice

Full question →

A data scientist is troubleshooting a failed SageMaker training job that uses a custom Docker image. The failure reason shows 'unrecognized arguments: --sagemaker_program'. What is the most likely cause?

Network Topology

A
The Docker image is tagged incorrectly and cannot be pulled
Why wrong: If the image could not be pulled, the failure reason would be different (e.g., 'CannotPullContainer').
B
The training job is in a different region than the ECR repository
Why wrong: Region mismatch would cause a different error, not an argument error.
C
The input mode is File mode, but the container expects Pipe mode
Why wrong: Input mode does not affect argument parsing.
D
The custom Docker image does not use the SageMaker training toolkit and thus does not accept SageMaker hyperparameters
Custom containers that are not toolkit-based ignore SageMaker hyperparameters, causing unrecognized argument errors if the entry point tries to parse them.

Full breakdown with real-world context →

Question 6hardmultiple choice

Full question →

A data engineer is troubleshooting an AWS Glue job that reads from and writes to the S3 bucket 'data-lake-bucket'. The job fails when trying to write to the 'sensitive/' prefix. The IAM policy attached to the Glue job's IAM role is shown in the exhibit. What is the MOST likely reason for the failure?

Exhibit

Refer to the exhibit.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:PutObject",
        "s3:DeleteObject"
      ],
      "Resource": "arn:aws:s3:::data-lake-bucket/*"
    },
    {
      "Effect": "Deny",
      "Action": "s3:PutObject",
      "Resource": "arn:aws:s3:::data-lake-bucket/sensitive/*"
    }
  ]
}

A
The IAM role does not have permission to read objects from the bucket
Why wrong: The policy allows s3:GetObject on all objects in the bucket.
B
The IAM role has an explicit deny for s3:PutObject on the 'sensitive/' prefix
The Deny statement blocks write access to the sensitive prefix.
C
The IAM policy does not specify the bucket resource correctly
Why wrong: The resource ARN is correctly specified for objects in the bucket.
D
The IAM policy lacks a required condition for encryption
Why wrong: There is no condition required; the deny is explicit.

Full breakdown with real-world context →

Question 7mediummulti select

Full question →

A company is building a sentiment analysis model for customer reviews. The dataset is balanced with 10,000 positive and 10,000 negative reviews. The model achieves 95% accuracy on the test set but fails to generalize to new reviews from a different product category. Which TWO techniques can improve generalization?

A
Increase the training dataset size by collecting more reviews
Why wrong: More data helps, but the question asks for techniques that can be applied to the existing scenario; collecting more data may not be immediately possible.
B
Use stratified k-fold cross-validation during training
Cross-validation provides a more reliable estimate of generalization and helps tune hyperparameters.
C
Apply L2 regularization to the model
Regularization penalizes large weights and reduces overfitting.
D
Add more features like review length and word count
Why wrong: Adding more features may lead to overfitting if not carefully engineered.
E
Use a more complex model with more layers
Why wrong: More complexity increases the risk of overfitting.

Full breakdown with real-world context →

Question 8mediummultiple choice

Full question →

Refer to the exhibit. A data scientist is unable to query a table in Amazon Athena that is located in the 'my-data-bucket' S3 bucket. The IAM policy shown is attached to the scientist's role. What is the most likely reason for the failure?

Exhibit

Refer to the exhibit.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:ListBucket"
      ],
      "Resource": [
        "arn:aws:s3:::my-data-bucket/*",
        "arn:aws:s3:::my-data-bucket"
      ]
    },
    {
      "Effect": "Allow",
      "Action": [
        "athena:StartQueryExecution",
        "athena:GetQueryResults"
      ],
      "Resource": "*"
    }
  ]
}

A
The policy does not allow decrypting data encrypted with AWS KMS.
Why wrong: No KMS actions are mentioned, but the policy does not prevent decryption either; the error is likely missing S3 write.
B
The policy does not allow athena:StartQueryExecution.
Why wrong: athena:StartQueryExecution is allowed.
C
The policy does not allow s3:GetObject on the bucket.
Why wrong: s3:GetObject is allowed.
D
The policy does not allow s3:PutObject to write query results to an S3 bucket.
Athena writes results to S3, requiring s3:PutObject.

Full breakdown with real-world context →

These MLS-C01 practice questions are part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style MLS-C01 questions with detailed explanations, topic-based practice, mock exams, readiness tracking, and study analytics.

Troubleshooting Scenario Questions

How to approach troubleshooting scenario questions

Quick answer

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MLS-C01 scenario practice questions

MLS-C01 troubleshooting practice questions

Practice scenarios

During EDA, a data scientist notices that a numeric feature 'age' has values ranging from 0 to 150, but expects adult ages between 18-100. Which TWO steps should the scientist take to investigate?

Refer to the exhibit. A data scientist is unable to run an Amazon Athena query on data in `my-bucket`. The IAM policy shown is attached to the user. What is the most likely reason for the failure?

Exhibit

A data scientist is troubleshooting a failed SageMaker training job that uses a custom Docker image. The failure reason shows 'unrecognized arguments: --sagemaker_program'. What is the most likely cause?

Exhibit

Refer to the exhibit. A data scientist is unable to query a table in Amazon Athena that is located in the 'my-data-bucket' S3 bucket. The IAM policy shown is attached to the scientist's role. What is the most likely reason for the failure?

Exhibit