Refer to the Exhibit Practice Questions

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. A data scientist runs the following AWS Glue ETL job script (Spark) to prepare data for ML:

```python
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)

datasource = glueContext.create_dynamic_frame.from_options(
    connection_type = "s3",
    connection_options = {"paths": ["s3://bucket/input/"]},
    format = "csv",
    format_options = {"withHeader": True}
)

applymapping = ApplyMapping.apply(frame = datasource, mappings = [("id", "int", "id", "int"), ("value", "string", "value", "double")])
...
```

The job fails with an error: "Job run failed: org.apache.spark.SparkException: Job aborted due to stage failure: Task failed while writing rows." What is the most likely cause of this error?

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?

{
    "TrainingJobName": "my-training-job",
    "TrainingJobStatus": "Failed",
    "FailureReason": "ClientError: Cannot evaluate expression: loss",
    "AlgorithmSpecification": {
        "TrainingImage": "123456789012.dkr.ecr.us-east-1.amazonaws.com/custom-latest",
        "TrainingInputMode": "File"
    },
    "ResourceConfig": {
        "InstanceType": "ml.m5.large",
        "InstanceCount": 1,
        "VolumeSizeInGB": 30
    },
    "StoppingCondition": {
        "MaxRuntimeInSeconds": 86400
    },
    "OutputDataConfig": {
        "S3OutputPath": "s3://my-bucket/output"
    }
}

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

{
    "TrainingJobName": "job-123",
    "TrainingJobStatus": "Failed",
    "FailureReason": "ClientError: Review the error message. Training failed due to insufficient instance memory.",
    "AlgorithmSpecification": {
        "TrainingImage": "123456789012.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.0-1",
        "TrainingInputMode": "File"
    },
    "ResourceConfig": {
        "InstanceType": "ml.m5.large",
        "InstanceCount": 1,
        "VolumeSizeInGB": 30
    }
}

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

ProcessingJobError: Execution failed
Error: Traceback (most recent call last):
  File "/opt/ml/processing/input/code/preprocess.py", line 45, in <module>
    df['age'] = df['age'].apply(float)
ValueError: could not convert string to float: 'twenty-five'

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

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"]}'

Refer to the exhibit.

```
Training Job Name: my-training-job
Status: Failed
Failure Reason: ClientError: Data download failed. Unable to locate credentials. Please configure your SageMaker Execution Role with the necessary permissions.
```
This is the output from `aws sagemaker describe-training-job --training-job-name my-training-job`.

Refer to the exhibit.

SageMaker Model Monitor schedule configuration:
```
{
  "ScheduleConfig": {
    "ScheduleExpression": "cron(0 * * * ? *)",
    "DataAnalysisStartTime": "2023-01-01T00:00:00Z",
    "DataAnalysisEndTime": "2023-01-01T23:59:00Z"
  },
  "JobDefinition": {
    "Environment": {
      "output_path": "s3://my-bucket/reports/"
    }
  },
  "MonitoringType": "DataQuality"
}
```

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?

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.