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Echte und neueste AWS-Certified-Machine-Learning-Specialty Fragen und Antworten der Amazon AWS-Certified-Machine-Learning-Specialty Zertifizierungsprüfung

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Amazon AWS Certified Machine Learning - Specialty AWS-Certified-Machine-Learning-Specialty Prüfungsfragen mit Lösungen (Q275-Q280):

275. Frage
A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes Which function will produce the desired output?

A. Smooth L1 loss
B. Rectified linear units (ReLU)
C. Softmax
D. Dropout

Antwort: B

276. Frage
A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test data. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.

Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

A. Lower the min_child_weight parameter value.
B. Lower the max_depth parameter value.
C. Update the objective to binary:logistic.
D. Increase the max_depth parameter value.

Antwort: B

Begründung:
Explanation
Overfitting occurs when a model performs well on the training data but poorly on the test data. This is often because the model has learned the training data too well and is not able to generalize to new data. To avoid overfitting, the Machine Learning Specialist should lower the max_depth parameter value. This will reduce the complexity of the model and make it less likely to overfit. According to the XGBoost documentation1, the max_depth parameter controls the maximum depth of a tree and lower values can help prevent overfitting. The documentation also suggests other ways to control overfitting, such as adding randomness, using regularization, and using early stopping1. References:
XGBoost Parameters

277. Frage
A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena The dataset contains more than 800.000 records stored as plaintext CSV files Each record contains 200 columns and is approximately 1 5 MB in size Most queries will span 5 to 10 columns only How should the Machine Learning Specialist transform the dataset to minimize query runtime?

A. Convert the records to GZIP CSV format
B. Convert the records to XML format
C. Convert the records to JSON format
D. Convert the records to Apache Parquet format

Antwort: D

278. Frage
A company is building a predictive maintenance model based on machine learning (ML). The data is stored in a fully private Amazon S3 bucket that is encrypted at rest with AWS Key Management Service (AWS KMS) CMKs. An ML specialist must run data preprocessing by using an Amazon SageMaker Processing job that is triggered from code in an Amazon SageMaker notebook. The job should read data from Amazon S3, process it, and upload it back to the same S3 bucket. The preprocessing code is stored in a container image in Amazon Elastic Container Registry (Amazon ECR). The ML specialist needs to grant permissions to ensure a smooth data preprocessing workflow.
Which set of actions should the ML specialist take to meet these requirements?

A. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Set up an S3 endpoint in the default VPC. Create Amazon SageMaker Processing jobs with the access key and secret key of the IAM user with appropriate KMS and ECR permissions.
B. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions.
C. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs, S3 read and write access to the relevant S3 bucket, and appropriate KMS and ECR permissions. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job from the notebook.
D. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs and to access Amazon ECR. Attach the role to the SageMaker notebook instance. Set up both an S3 endpoint and a KMS endpoint in the default VPC. Create Amazon SageMaker Processing jobs from the notebook.

Antwort: B

Begründung:
The correct solution for granting permissions for data preprocessing is to use the following steps:
* Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. This role allows the ML specialist to run Processing jobs from the notebook code1
* Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions. This role allows the Processing job to access the data in the encrypted S3 bucket, decrypt it with the KMS CMK, and pull the container image from ECR23 The other options are incorrect because they either miss some permissions or use unnecessary steps. For example:
* Option A uses a single IAM role for both the notebook instance and the Processing job. This role may have more permissions than necessary for the notebook instance, which violates the principle of least privilege4
* Option C sets up both an S3 endpoint and a KMS endpoint in the default VPC. These endpoints are not required for the Processing job to access the data in the encrypted S3 bucket. They are only needed if the Processing job runs in network isolation mode, which is not specified in the question.
* Option D uses the access key and secret key of the IAM user with appropriate KMS and ECR permissions. This is not a secure way to pass credentials to the Processing job. It also requires the ML specialist to manage the IAM user and the keys.
References:
* 1: Create an Amazon SageMaker Notebook Instance - Amazon SageMaker
* 2: Create a Processing Job - Amazon SageMaker
* 3: Use AWS KMS-Managed Encryption Keys - Amazon Simple Storage Service
* 4: IAM Best Practices - AWS Identity and Access Management
* : Network Isolation - Amazon SageMaker
* : Understanding and Getting Your Security Credentials - AWS General Reference

279. Frage
A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.
The company requires at least 85% accuracy to make use of the model.
After an exhaustive grid search, the optimal hyperparameters produced the following:
68% accuracy on the training set
67% accuracy on the validation set
What can the machine learning specialist do to improve the system's accuracy?

A. Add more data to the training set and retrain the model using transfer learning to reduce the bias.
B. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.
C. Train a new model using the current neural network architecture.
D. Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.

Antwort: A

280. Frage
......

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Echte und neueste AWS-Certified-Machine-Learning-Specialty Fragen und Antworten der Amazon AWS-Certified-Machine-Learning-Specialty Zertifizierungsprüfung

AWS-Certified-Machine-Learning-Specialty AWS Certified Machine Learning - Specialty Pass4sure Zertifizierung & AWS Certified Machine Learning - Specialty zuverlässige Prüfung Übung

Amazon AWS Certified Machine Learning - Specialty AWS-Certified-Machine-Learning-Specialty Prüfungsfragen mit Lösungen (Q275-Q280):

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