Courseiva
← Back to AWS Certified Machine Learning Specialty MLS-C01 questions

Scenario-based practice

Refer to the Exhibit Practice 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.

Start full practice testRead exam guide
15
scenario questions
MLS-C01
exam code
Amazon Web Services
vendor

Scenario guide

How to approach refer to the exhibit practice questions

Practise exhibit-style questions that ask you to read a topology, table, command output or diagram before choosing the best answer.

Quick answer

Exhibit-style questions test whether you can read a topology, command output, diagram or table before choosing the best answer.

How to extract the relevant detail from an exhibit.

How topology, command output or routing information affects the answer.

How to avoid answering from memory before reading the evidence.

How to map the exhibit back to the exam objective.

Related practice questions

Related MLS-C01 topic practice pages

Scenario questions usually connect to one or more exam topics. Use these links to review the underlying concepts behind the scenario.

Data Engineering practice questions

Work through MLS-C01 questions on Data Engineering.

Machine Learning Implementation and Operations practice questions

Targeted MLS-C01 practice covering Machine Learning Implementation and Operations.

Modeling practice questions

Work through MLS-C01 questions on Modeling.

Exploratory Data Analysis practice questions

Targeted MLS-C01 practice covering Exploratory Data Analysis.

MLS-C01 fundamentals practice questions

Practise MLS-C01 questions linked to MLS-C01 fundamentals.

MLS-C01 scenario practice questions

Targeted MLS-C01 practice covering MLS-C01 scenario.

MLS-C01 troubleshooting practice questions

Work through MLS-C01 questions on MLS-C01 troubleshooting.

Practice set

Practice scenarios

Question 1mediummultiple choice
Full question →

A data engineer runs the AWS CLI command shown in the exhibit to find large log files in S3. The command returns an empty list, but the engineer knows there are files larger than 1 MB in that prefix. What is the MOST likely issue?

Network Topology
aws s3api list-objectsbucket my-data-lakeprefix logs/2023/12/01/Refer to the exhibit.
Question 2mediummultiple choice
Full question →

Refer to the exhibit. A data scientist creates a SageMaker model using the configuration above. When deploying the model to an endpoint, the endpoint status remains 'Creating' for a long time and then fails. What is the most likely cause?

Exhibit

Refer to the exhibit.
```
{
  "ModelName": "my-model",
  "PrimaryContainer": {
    "Image": "382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:1",
    "ModelDataUrl": "s3://my-bucket/model.tar.gz",
    "Environment": {
      "SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
      "SAGEMAKER_REGION": "us-east-1"
    }
  },
  "ExecutionRoleArn": "arn:aws:iam::123456789012:role/SageMakerRole"
}
```
Question 3hardmultiple choice
Full question →

Refer to the exhibit. A data scientist runs the above AWS CLI command to create a SageMaker training job using the built-in Linear Learner algorithm. The training job fails with an error. What is the most likely cause?

Exhibit

Refer to the exhibit.

```
aws sagemaker create-training-job \
    --training-job-name my-training-job \
    --algorithm-specification TrainingImage=382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:1,TrainingInputMode=File \
    --role-arn arn:aws:iam::123456789012:role/SageMakerRole \
    --input-data-config [{"ChannelName":"train","DataSource":{"S3DataSource":{"S3DataType":"AugmentedManifestFile","S3Uri":"s3://bucket/train.manifest","S3DataDistributionType":"FullyReplicated"}},"ContentType":"application/x-recordio"}] \
    --output-data-config S3OutputPath=s3://bucket/output \
    --resource-config InstanceType=ml.m5.large,InstanceCount=1,VolumeSizeInGB=10 \
    --stopping-condition MaxRuntimeInSeconds=3600
Question 4mediummultiple 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/*"
      ]
    }
  ]
}
```
Question 5hardmultiple choice
Full question →

Refer to the exhibit. A data scientist is running an Amazon EMR Spark job for exploratory data analysis on a large dataset. The job fails with the error shown. What is the most appropriate action to resolve this?

Exhibit

Refer to the exhibit.
```
$ cat /var/log/syslog | grep "OutOfMemory"
2024-01-15 10:30:45 ERROR OutOfMemoryError: Java heap space
   at org.apache.spark.sql.catalyst.expressions.GenerateMutableProjection.apply(Unknown Source)
```
Question 6hardmultiple choice
Full question →

A Glue job fails with an AccessDenied error when trying to write to the S3 bucket my-data-lake. The IAM policy attached to the job role is shown in the exhibit. What is the MOST likely reason for the failure?

Exhibit

Refer to the exhibit.

IAM policy attached to an AWS Glue job role:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:PutObject"
            ],
            "Resource": "arn:aws:s3:::my-data-lake/*"
        },
        {
            "Effect": "Allow",
            "Action": "s3:ListBucket",
            "Resource": "arn:aws:s3:::my-data-lake"
        }
    ]
}
Question 7mediummultiple choice
Full question →

A data scientist runs the AWS CLI command shown in the exhibit to list objects larger than 100 KB in an S3 bucket. The data scientist wants to understand the size distribution of these files. What is the most significant limitation of this approach for EDA?

Network Topology
aws s3api list-objectsbucket my-bucketprefix data/Refer to the exhibit.```
Question 8mediummultiple choice
Full question →

Refer to the exhibit. A data scientist is assigned an IAM policy to deploy a SageMaker model. When the scientist tries to create an endpoint, the action fails with an authorization error. What is the missing permission?

Exhibit

Refer to the exhibit.

```
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "sagemaker:CreateTrainingJob",
                "sagemaker:CreateModel",
                "sagemaker:CreateEndpointConfig",
                "sagemaker:CreateEndpoint"
            ],
            "Resource": "*"
        }
    ]
}
```
Question 9hardmultiple choice
Full question →

Refer to the exhibit. A data scientist is trying to run a SageMaker training job using a script that reads training data from 's3://my-bucket/training/data.csv'. The job fails with an access denied error. What is the MOST likely reason?

Exhibit

Refer to the exhibit.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:DescribeTrainingJob"
      ],
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::my-bucket/training/*"
    }
  ]
}
Question 10mediummultiple choice
Full question →

Refer to the exhibit. A company is using an IAM role with the attached policy to deploy a SageMaker model. The data scientist can create training jobs and models, but when trying to create an endpoint, they receive an access denied error. What is the missing permission?

Exhibit

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:PutObject"
      ],
      "Resource": "arn:aws:s3:::my-bucket/*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:CreateModel",
        "sagemaker:CreateEndpointConfig",
        "sagemaker:CreateEndpoint"
      ],
      "Resource": "*"
    }
  ]
}
Question 11hardmultiple choice
Full question →

Refer to the exhibit. A data scientist is trying to create a SageMaker training job but receives an access denied error. The IAM policy shown is attached to their role. What is the most likely reason for the error?

Exhibit

Refer to the exhibit.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "sagemaker:CreateTrainingJob",
      "Resource": "*",
      "Condition": {
        "StringEquals": {
          "sagemaker:TrainingJobStatus": "Failed"
        }
      }
    }
  ]
}
Question 12mediummultiple choice
Full question →

Refer to the exhibit. An ML engineer runs the above CLI command to inspect files in an S3 bucket. The training data consists of 200 CSV files, each 1 GB. The engineer plans to use Amazon SageMaker to train a model using this data. What should the engineer do to optimize training performance?

Network Topology
$ aws s3api list-objects-v2bucket my-ml-dataprefix training/max-items 2Refer to the exhibit."Contents": ["Key": "training/data1.csv","Size": 1073741824},"Key": "training/data2.csv",],"IsTruncated": true,"NextToken": "eyJ2IjoiMSJ9..."
Question 13mediummultiple choice
Full question →

Refer to the exhibit. A data scientist runs the AWS CLI command to create a SageMaker training job. The job fails immediately with 'ValidationException: Invalid instance type'. What is the most likely issue?

Exhibit

Refer to the exhibit.

$ aws sagemaker create-training-job \
    --training-job-name my-job \
    --algorithm-specification TrainingImage=382416733822.dkr.ecr.us-west-2.amazonaws.com/xgboost:latest,TrainingInputMode=File \
    --role-arn arn:aws:iam::123456789012:role/SageMakerRole \
    --input-data-config "[{\"ChannelName\": \"train\", \"DataSource\": {\"S3DataSource\": {\"S3DataType\": \"S3Prefix\", \"S3Uri\": \"s3://my-bucket/train/\", \"S3DataDistributionType\": \"FullyReplicated\"}}}]" \
    --output-data-config "{\"S3OutputPath\": \"s3://my-bucket/output/\"}" \
    --resource-config "{\"InstanceType\": \"ml.m5.large\", \"InstanceCount\": 1}" \
    --stopping-condition "{\"MaxRuntimeInSeconds\": 86400}"
Question 14hardmultiple choice
Full question →

A data scientist attempts to create a SageMaker training job using the IAM policy shown in the exhibit. The training job fails with an access denied error. What is the most likely cause?

Exhibit

Refer to the exhibit.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "sagemaker:CreateTrainingJob",
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::my-bucket/*"
    }
  ]
}
Question 15mediummultiple choice
Full question →

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance role. When the data scientist tries to run a training job that writes model artifacts to 's3://my-bucket/models/', the job fails with an access denied error. What is the MOST likely cause?

Exhibit

Refer to the exhibit.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:DescribeTrainingJob"
      ],
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject"
      ],
      "Resource": "arn:aws:s3:::my-bucket/training-data/*"
    }
  ]
}

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.