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You need to specify the InputDataConfig, but it does not need to be "S3"
I think the reason why A and B are wrong, not because data location is not required, but because it doesn't need to be S3, it can be Amazon S3, EFS, or FSx location
From here https://docs.aws.amazon.com/zh_tw/sagemaker/latest/dg/API_CreateTrainingJob.html .. the only "Required: Yes" attributes are:
1. AlgorithmSpecification (in this TrainingInputMode is Required - i.e. File or Pipe)
2. OutputDataConfig (in this S3OutputPath is Required - where the model artifacts are stored)
3. ResourceConfig (in this EC2 InstanceType and VolumeSizeInGB are required)
4. RoleArn (..The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf...the caller of this API must have the iam:PassRole permission.)
5. StoppingCondition
6. TrainingJobName (The name of the training job. The name must be unique within an AWS Region in an AWS account.)
From the given options in the questions.. we have 2, 3, and 4 above. so, the answer is CEF.
This is the best explanation that CEF is the right answer, IMO. The document at that url is very informative. It also specifically states that InputDataConfig is NOT required. Having said that, I have no idea how the model will train if it doesn't know where to find the training data, but that is what the document says. If someone can explain that, I'd like to hear the explanation.
If I see this question on the actual exam, I'm going with AEF. The model absolutely must know where the training data is. I have seen other documentation that does confirm that you need the location of the input data, the compute instance and location to output the model artifacts.
but you also need to specify the service role sagemaker should use otherwise it will not be able to perform actions on your behalf like provisioning the training instances.
The question is asking about built in algorithms. It should be ADE. See https://docs.aws.amazon.com/zh_tw/sagemaker/latest/dg/API_CreateTrainingJob.html
for "3. ResourceConfig", only VolumeSizeInGB is required. So, it's not about the instance type.
Check: https://docs.aws.amazon.com/zh_tw/sagemaker/latest/APIReference/API_ResourceConfig.html
The input channel and output channel are mandatory, as the training job needs to know where to get the input data from and where to publish the model artifact. IAM role is also needed, for AWS services. others are not mandatory, validation channel is not mandatory for instance in case of unsupervised learning, likewise hyper params can be be auto tuned for as well as the ec2 instance types can be default ones that will be picked
As they narrowed it to S3, A is incorrect BUT when submitting Amazon SageMaker training jobs using one of the built-in algorithms, it is a MUST to identify the location of training data. While Amazon S3 is commonly used for storing training data, other sources like Docker containers, DynamoDB, or local disks of training instances can also be used. Therefore, specifying the location of training data is essential for SageMaker to know where to access the data during training.
So the right answer is CEF for me for this case... However if A was saying identify the location of training data, I think option A would be included in the MUST parameter.
InputDataConffig is optional in create_training_job.Please check thte parameters that are required.
So answer is CEF: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html
InputDataConffig is optional in create_training_job.Please check thte parameters that are required.
So answer is SEF: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html
E is not important, some models could simply work on the default of CPU.
A is a must and E is a must too.
C is important for permission handling on S3 etc.
It has to be A, C, F
A. The training channel identifying the location of training data on an Amazon S3 bucket: This is where SageMaker will get the input data for training the model.
C. The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users: This role provides SageMaker the necessary permissions to access AWS resources.
F. The output path specifying where on an Amazon S3 bucket the trained model will persist: After training, the model artifacts need to be saved in a specified S3 bucket location.
A. The training channel identifying the location of training data on an Amazon S3 bucket: This is essential because SageMaker needs to know where to find the data for training.
C. The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users: SageMaker requires permissions to access resources on behalf of the user, and this is provided by specifying an IAM role with the necessary policies attached.
F. The output path specifying where on an Amazon S3 bucket the trained model will persist: After the model is trained, SageMaker needs to save the output, which includes the model artifacts, to a specified S3 location.
https://docs.aws.amazon.com/zh_tw/sagemaker/latest/dg/API_CreateTrainingJob.html
The answer should be CEF
The only attributes strictly required("Required: Yes" ) are:
TrainingJobName
AlgorithmSpecification
OutputDataConfig
ResourceConfig
RoleArn
StoppingCondition
So, why is 'InputDataConfig' strictly required?
When 'InputDataConfig' is not needed:
Algorithm with Pre-loaded Data where the data is already embedded or hardcoded within the training script or Docker container or pre-defined datasets available within SageMaker
Algorithm Generating (training) Data such as synthetic data generation or reinforcement learning scenarios
Can confirm, the answer is CEF...
I just went into SageMaker console, and tried to create a training job...
You must have an IAM Role for permissions.
Leaving the instance class/instance type blank is not possible it is always autofilled with a default instance, therefore REQUIRED.
Output path for the model after training is also required, you cannot create a training job without these fields... therefore the answer is CEF
- IAM Role
- Instance Class/Type
- Output path for completed model
Required Parameters:
Sagemaker Required Parameters
Algorothm Specs - path to docker image
Output Data Config - (path to S3)
Resource Confg - Instance type and storage volume
Role ARN
Stopping Conditions
Training job Name
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