Refer to the Exhibit Practice Questions

Refer to the exhibit.

aws sagemaker create-training-job \
    --training-job-name my-training-job \
    --algorithm-specification 'TrainingImage=123456789012.dkr.ecr.us-west-2.amazonaws.com/my-custom-training:latest,TrainingInputMode=File' \
    --role-arn arn:aws:iam::123456789012:role/SageMakerExecutionRole \
    --input-data-config '[{"ChannelName":"train","DataSource":{"S3DataSource":{"S3Uri":"s3://my-bucket/train/","S3DataType":"S3Prefix"}},"ContentType":"text/csv"}]' \
    --output-data-config '{"S3OutputPath":"s3://my-bucket/output/"}' \
    --resource-config '{"InstanceType":"ml.m5.large","InstanceCount":1,"VolumeSizeInGB":30}' \
    --vpc-config '{"SecurityGroupIds":["sg-12345678"],"Subnets":["subnet-12345678"]}'

{
  "PipelineExperimentConfig": {
    "ExperimentName": "my-experiment",
    "TrialName": "my-trial"
  },
  "Parameters": {
    "TrainingInstanceType": "ml.m5.large",
    "TrainingInstanceCount": 2,
    "MaxRuntimeInSeconds": 86400
  },
  "Steps": [
    {
      "Name": "Preprocess",
      "Type": "Processing",
      "ProcessingJobName": "preprocess-job",
      "ProcessingResources": {
        "ClusterConfig": {
          "InstanceCount": 1,
          "InstanceType": "ml.m5.large",
          "VolumeSizeInGB": 30
        }
      }
    },
    {
      "Name": "Train",
      "Type": "Training",
      "TrainingJobName": "train-job",
      "AlgorithmSpecification": {
        "TrainingImage": "123456789012.dkr.ecr.us-east-1.amazonaws.com/my-algo:latest",
        "TrainingInputMode": "File"
      },
      "ResourceConfig": {
        "InstanceCount": 2,
        "InstanceType": "ml.m5.large",
        "VolumeSizeInGB": 30
      }
    }
  ]
}

Refer to the exhibit. A data scientist is trying to run a SageMaker Processing job that reads data from an S3 bucket. The IAM role attached to the processing job has the following policy:

```json
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "s3:GetObject",
            "Resource": "arn:aws:s3:::my-bucket/*"
        }
    ]
}
```

The job fails with an error: "Access Denied" when trying to list objects. What is the root cause?

An AWS Glue job fails with the following error from the CloudWatch logs:
"Conversion error: Unable to convert column 'price' from String to Double for some rows."

{
  "MonitoringScheduleName": "model-quality-monitor",
  "EndpointName": "my-endpoint",
  "MonitoringType": "DataQuality",
  "MonitoringScheduleConfig": {
    "ScheduleExpression": "cron(0 * * * ? *)",
    "MonitoringJobDefinition": {
      "BaselineConfig": {
        "BaseliningJobName": "baseline-job",
        "ConstraintsResource": {
          "S3Uri": "s3://my-bucket/baseline/constraints.json"
        },
        "StatisticsResource": {
          "S3Uri": "s3://my-bucket/baseline/statistics.json"
        }
      },
      "MonitoringInputs": [
        {
          "EndpointInput": {
            "EndpointName": "my-endpoint",
            "LocalPath": "/opt/ml/processing/input/endpoint",
            "S3DataDistributionType": "FullyReplicated",
            "S3InputMode": "File"
          }
        }
      ],
      "MonitoringOutputConfig": {
        "MonitoringOutputs": [
          {
            "S3Output": {
              "S3Uri": "s3://my-bucket/monitor-output",
              "LocalPath": "/opt/ml/processing/output",
              "S3UploadMode": "Continuous"
            }
          }
        ]
      },
      "MonitoringResources": {
        "ClusterConfig": {
          "InstanceCount": 1,
          "InstanceType": "ml.m5.large",
          "VolumeSizeInGB": 20
        }
      },
      "RoleArn": "arn:aws:iam::123456789012:role/SageMakerMonitorRole"
    }
  }
}

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:DescribeEndpoint",
        "sagemaker:ListEndpoints"
      ],
      "Resource": "*"
    }
  ]
}

TrainingStep(
    name="TrainModel",
    step_args=train_args,
    depends_on=[tuning_step]
)
tuning_step = TuningStep(...) # produces multiple artifacts

Refer to the exhibit. A data scientist uses the following SageMaker Feature Store feature definition (using the Boto3 SDK) to create a feature group:

```python
import boto3
sagemaker = boto3.client('sagemaker', region_name='us-east-1')
response = sagemaker.create_feature_group(
    FeatureGroupName='my-feature-group',
    RecordIdentifierFeatureName='customer_id',
    EventTimeFeatureName='timestamp',
    FeatureDefinitions=[
        {'FeatureName': 'customer_id', 'FeatureType': 'String'},
        {'FeatureName': 'age', 'FeatureType': 'String'},
        {'FeatureName': 'income', 'FeatureType': 'Fractional'}
    ],
    OnlineStoreConfig={'EnableOnlineStore': True},
    RoleArn='arn:aws:iam::123456789012:role/SageMakerRole'
)
```

The data scientist later tries to ingest data with an 'age' column containing integer values. The ingestion fails. What is the most likely reason?

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job

args = getResolvedOptions(sys.argv, ['JOB_NAME'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

raw = glueContext.create_dynamic_frame.from_options(
    connection_type="s3",
    connection_options={"paths": ["s3://bucket/input/year=2023/month=01/"]},
    format="json")

transformed = raw.select_fields(["col1", "col2"]).rename_field("col1", "new_col")

glueContext.write_dynamic_frame.from_options(
    frame=transformed,
    connection_type="s3",
    connection_options={"path": "s3://bucket/output/transformed"},
    format="parquet")

job.commit()

Refer to the exhibit.

[ERROR] 2022-12-01 10:15:30,123 – model_server – ModelLoadFailed: Unable to load model from /opt/ml/model. Parsed error: FileNotFoundError: [Errno 2] No such file or directory: '/opt/ml/model/classes.txt'

This log is from a SageMaker endpoint instance. The model was packaged as a tar.gz containing model.pth, classes.txt, and inference.py. The Docker container uses the SageMaker inference toolkit.

Refer to the exhibit.

{
  "EndpointName": "my-endpoint",
  "EndpointConfigName": "my-endpoint-config-v1",
  "ProductionVariants": [
    {
      "VariantName": "v1",
      "DeployedImages": [
        {
          "SpecifiedImage": "123456789012.dkr.ecr.us-west-2.amazonaws.com/my-model:1",
          "ResolvedImage": "123456789012.dkr.ecr.us-west-2.amazonaws.com/my-model:1@sha256:abc123"
        }
      ],
      "CurrentWeight": 1.0,
      "DesiredWeight": 1.0,
      "CurrentInstanceCount": 2,
      "DesiredInstanceCount": 2
    }
  ],
  "EndpointStatus": "InService"
}

[2024-01-15 10:30:45] Training job 'my-training-job' started.
[2024-01-15 10:31:10] Using algorithm 'built-in' with hyperparameters: {'epochs': 10, 'batch-size': 32, 'learning-rate': 0.001}
[2024-01-15 10:31:15] File system creation failed: No usable scratch space. Error: Input/output error.
[2024-01-15 10:31:15] Retrying with local SSD...
[2024-01-15 10:31:20] Training completed with status 'Failed'.

{
  "EndpointConfigName": "my-config",
  "ProductionVariants": [
    {
      "VariantName": "variant1",
      "ModelName": "my-model-v1",
      "InitialInstanceCount": 1,
      "InstanceType": "ml.c5.large",
      "InitialVariantWeight": 1.0
    }
  ]
}