QUESTION: 113

A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.

Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?

Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.
Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.
Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.
Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.

Answer(s): B

Reference:

https://sagemaker.readthedocs.io/en/stable/frameworks/tensorflow/using_tf.html
https://github.com/aws-samples/amazon-sagemaker-script-mode/blob/master/tf-horovod-inference-pipeline/train.py

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QUESTION: 114

The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data.

Which machine learning algorithm should the researchers use that BEST meets their requirements?

Latent Dirichlet Allocation (LDA)
Recurrent neural network (RNN)
K-means
Convolutional neural network (CNN)

Answer(s): D

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QUESTION: 115

A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.

How should a data scientist adjust the solution?

Use the event tracker in Amazon Personalize to include real-time user interactions.
Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
Add event type and event value fields to the interactions dataset in Amazon Personalize.

Answer(s): A

Explanation:

Because in this case, it is not the problem with the existing historical data (event value, event type(click or not)), the sales do not keep growing and now you need to obtain more recent interactive data. An event tracker specifies a destination dataset group for new event data.

Reference:

https://docs.aws.amazon.com/personalize/latest/dg/maintaining-relevance.html

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QUESTION: 116

A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet.

Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)

Add a VPC endpoint policy to allow access to the IAM users.
Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.
Modify the security group on the endpoint network interface to restrict access to the instances.
Modify the ACL on the endpoint network interface to restrict access to the instances.
Add a SageMaker Runtime VPC endpoint interface to the VPC.

Answer(s): A,C

Reference:

https://aws.amazon.com/blogs/machine-learning/private-package-installation-in-amazon-sagemaker-running-in-internet-free-mode/

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QUESTION: 115

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QUESTION: 116

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