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Free PDF AWS-Certified-Machine-Learning-Specialty Braindumps Downloads | Perfect Reliable AWS-Certified-Machine-Learning-Specialty Guide Files: AWS Certified Machine Learning - Specialty

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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Certification Exam is designed to assess the knowledge and skills of individuals in the field of machine learning. AWS Certified Machine Learning - Specialty certification is intended for professionals who have experience in building, training, and deploying machine learning models on the Amazon Web Services (AWS) platform. AWS-Certified-Machine-Learning-Specialty exam tests the ability of candidates to design and implement machine learning solutions using AWS services.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Certification Exam is a highly sought-after certification in the field of Machine Learning. It is designed for individuals who have a strong understanding of machine learning concepts and techniques and are interested in showcasing their expertise in this domain. AWS Certified Machine Learning - Specialty certification is specifically designed for professionals who are interested in building, deploying, and maintaining machine learning solutions on Amazon Web Services (AWS) platform.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q325-Q330):

NEW QUESTION # 325
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?

  • A. 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.
  • B. Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.
  • C. 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.
  • D. 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.

Answer: A

Explanation:
Amazon SageMaker script mode is a feature that allows users to use training scripts similar to those they would use outside SageMaker with SageMaker's prebuilt containers for various frameworks such as TensorFlow. Script mode supports reading data from Amazon S3 buckets without requiring any changes to the training script. Therefore, option B is the best method of providing training data to Amazon SageMaker that would meet the business requirements with the least development overhead.
Option A is incorrect because using a local path of the data would not be scalable or reliable, as it would depend on the availability and capacity of the local storage. Moreover, using a local path of the data would not leverage the benefits of Amazon S3, such as durability, security, and performance. Option C is incorrect because rewriting the train.py script to convert TFRecords to protobuf would require additional development effort and complexity, as well as introduce potential errors and inconsistencies in the data format. Option D is incorrect because preparing the data in the format accepted by Amazon SageMaker would also require additional development effort and complexity, as well as involve using additional services such as AWS Glue or AWS Lambda, which would increase the cost and maintenance of the solution.
Bring your own model with Amazon SageMaker script mode
GitHub - aws-samples/amazon-sagemaker-script-mode
Deep Dive on TensorFlow training with Amazon SageMaker and Amazon S3
amazon-sagemaker-script-mode/generate_cifar10_tfrecords.py at master


NEW QUESTION # 326
An obtain relator collects the following data on customer orders: demographics, behaviors, location, shipment progress, and delivery time. A data scientist joins all the collected datasets. The result is a single dataset that includes 980 variables.
The data scientist must develop a machine learning (ML) model to identify groups of customers who are likely to respond to a marketing campaign.
Which combination of algorithms should the data scientist use to meet this requirement? (Select TWO.)

  • A. Se mantic feg mentation
  • B. Factorization machines (FM)
  • C. K-means
  • D. Principal component analysis (PCA)
  • E. Latent Dirichlet Allocation (LDA)

Answer: C,D

Explanation:
The data scientist should use K-means and principal component analysis (PCA) to meet this requirement. K-means is a clustering algorithm that can group customers based on their similarity in the feature space. PCA is a dimensionality reduction technique that can transform the original 980 variables into a smaller set of uncorrelated variables that capture most of the variance in the data. This can help reduce the computational cost and noise in the data, and improve the performance of the clustering algorithm.
References:
Clustering - Amazon SageMaker
Dimensionality Reduction - Amazon SageMaker


NEW QUESTION # 327
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?

  • A. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
  • B. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
  • C. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
  • D. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.

Answer: C

Explanation:
Amazon SageMaker notebook instances are fully managed environments that provide an integrated Jupyter notebook interface for data exploration, analysis, and machine learning. Amazon SageMaker notebook instances are based on EC2 instances that run within AWS service accounts, not within customer accounts. This means that the ML Specialist cannot find the Amazon SageMaker notebook instance's EC2 instance or EBS volume within the VPC, as they are not visible or accessible to the customer. However, the ML Specialist can still take a snapshot of the EBS volume by using the Amazon SageMaker console or API. The ML Specialist can also use VPC interface endpoints to securely connect the Amazon SageMaker notebook instance to the resources within the VPC, such as Amazon S3 buckets, Amazon EFS file systems, or Amazon RDS databases


NEW QUESTION # 328
A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data Which type of data repository is the MOST cost-effective solution?

  • A. An Amazon RDS database with hourly table partitions
  • B. An Amazon EBS-backed Amazon EC2 instance with hourly directories
  • C. An Amazon S3 data lake with hourly object prefixes
  • D. An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes

Answer: C

Explanation:
An Amazon S3 data lake is a cost-effective solution for storing and analyzing large amounts of time-based training data for a new model. Amazon S3 is a highly scalable, durable, and secure object storage service that can store any amount of data in any format. Amazon S3 also offers low-cost storage classes, such as S3 Standard-IA and S3 One Zone-IA, that can reduce the storage costs for infrequently accessed data. By using hourly object prefixes, the Machine Learning Specialist can organize the data into logical partitions based on the time of ingestion. This can enable efficient data access and management, as well as support incremental updates and deletes. The Specialist can also use Amazon S3 lifecycle policies to automatically transition the data to lower-cost storage classes or delete the data after a certain period of time. This way, the Specialist can always train on the last 24 hours of the data and optimize the storage costs.
References:
What is a data lake? - Amazon Web Services
Amazon S3 Storage Classes - Amazon Simple Storage Service
Managing your storage lifecycle - Amazon Simple Storage Service
Best Practices Design Patterns: Optimizing Amazon S3 Performance


NEW QUESTION # 329
A machine learning specialist is developing a proof of concept for government users whose primary concern is security. The specialist is using Amazon SageMaker to train a convolutional neural network (CNN) model for a photo classifier application. The specialist wants to protect the data so that it cannot be accessed and transferred to a remote host by malicious code accidentally installed on the training container.
Which action will provide the MOST secure protection?

  • A. Enable network isolation for training jobs.
  • B. Remove Amazon S3 access permissions from the SageMaker execution role.
  • C. Encrypt the training and validation dataset.
  • D. Encrypt the weights of the CNN model.

Answer: A

Explanation:
The most secure action to protect the data from being accessed and transferred to a remote host by malicious code accidentally installed on the training container is to enable network isolation for training jobs. Network isolation is a feature that allows you to run training and inference containers in internet-free mode, which blocks any outbound network calls from the containers, even to other AWS services such as Amazon S3.
Additionally, no AWS credentials are made available to the container runtime environment. This way, you can prevent unauthorized access to your data and resources by malicious code or users. You can enable network isolation by setting the EnableNetworkIsolation parameter to True when you call CreateTrainingJob, CreateHyperParameterTuningJob, or CreateModel.
References:
* Run Training and Inference Containers in Internet-Free Mode - Amazon SageMaker


NEW QUESTION # 330
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This format is for candidates who do not have the time or energy to use a computer or laptop for preparation. The Amazon AWS-Certified-Machine-Learning-Specialty PDF file includes real Amazon AWS-Certified-Machine-Learning-Specialty questions, and they can be easily printed and studied at any time. ValidVCE regularly updates its PDF file to ensure that its readers have access to the updated questions.

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