An ML engineer is building a model to predict house and apartment prices. The model uses three features:
Square Meters, Price, and Age of Building. The dataset has 10,000 data rows. The data includes data points for one large mansion and one extremely small apartment.
The ML engineer must perform preprocessing on the dataset to ensure that the model produces accurate predictions for the typical house or apartment.
Which solution will meet these requirements?

• A.Remove the outliers and perform a log transformation on the Square Meters variable.
• B.Keep the outliers and perform normalization on the Square Meters variable.
• C.Remove the outliers and perform one-hot encoding on the Square Meters variable.
• D.Keep the outliers and perform one-hot encoding on the Square Meters variable.

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A company plans to use Amazon SageMaker AI to build image classification models. The company has 6 TB of training data stored on Amazon FSx for NetApp ONTAP. The file system is in the same VPC as SageMaker AI.
An ML engineer must make the training data accessible to SageMaker AI training jobs.
Which solution will meet these requirements?

• A.Mount the FSx for ONTAP file system as a volume to the SageMaker AI instance.
• B.Create an Amazon S3 bucket and use Mountpoint for Amazon S3 to link the bucket to FSx for ONTAP.
• C.Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
• D.Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.

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An ML engineer is building a logistic regression model to predict customer churn for subscription services.
The dataset contains two string variables: location and job_seniority_level.
The location variable has 3 distinct values, and the job_seniority_level variable has over 10 distinct values.
The ML engineer must perform preprocessing on the variables.
Which solution will meet this requirement?

• A.Apply tokenization to location. Apply ordinal encoding to job_seniority_level.
• B.Apply one-hot encoding to location. Apply ordinal encoding to job_seniority_level.
• C.Apply binning to location. Apply standard scaling to job_seniority_level.
• D.Apply one-hot encoding to location. Apply standard scaling to job_seniority_level.

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An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.
Which solution will meet these requirements?

• A.Use Amazon Data Firehose to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
• B.Use AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
• C.Use Amazon Redshift ML to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
• D.Use Amazon Athena to create the data ingestion pipelines. Use an Amazon SageMaker notebook to create the model deployment pipelines.

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A company uses a training job on Amazon SageMaker Al to train a neural network. The job first trains a model and then evaluates the model's performance ag test dataset. The company uses the results from the evaluation phase to decide if the trained model will go to production.
The training phase takes too long. The company needs solutions that can shorten training time without decreasing the model's final performance.
Select the correct solutions from the following list to meet the requirements for each description. Select each solution one time or not at all. (Select THREE.)
. Change the epoch count.
. Choose an Amazon EC2 Spot Fleet.
Change the batch size.
. Use early stopping on the training job.
Use the SageMaker Al distributed data parallelism (SMDDP) library.
. Stop the training job.

Correct Answer:

Explanation:
Change the number of samples used in each iteration of training
Correct selection:
Change the batch size
Why:
Increasing the batch size reduces the number of iterations per epoch, which can significantly shorten training time while maintaining model quality when tuned appropriately. AWS explicitly recommends batch size tuning as a primary performance optimization.
Increase the number of instances used during training
Correct selection:
Use the SageMaker AI distributed data parallelism (SMDDP) library
Why:
SMDDP is designed to efficiently distribute training data across multiple GPU instances with optimized gradient synchronization. This accelerates training without affecting model convergence or accuracy, unlike naive scaling approaches.
Stop training before the maximum number of epochs are reached if performance is sufficient and not improving Correct selection:
Use early stopping on the training job
Why:
Early stopping automatically terminates training when validation metrics stop improving. AWS recommends this to reduce wasted compute time while preserving optimal model performance.

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