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Target Audience

The Amazon MLS-C01 Exam is targeted at those individuals who are tasked with performing the data science or development role. It provides that the candidates can design, deploy, implement, and maintain ML or machine learning solutions for given business problems.

Amazon MLS-C01 exam covers a wide range of topics related to machine learning, including data preparation, feature engineering, model training and evaluation, and deployment. Candidates are required to have a strong understanding of machine learning algorithms, statistical modeling, and programming languages such as Python and R. In addition, candidates are expected to have experience working with AWS services such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

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The AWS Certified Machine Learning - Specialty Exam covers a wide range of topics related to machine learning, including data preparation and feature engineering, model selection and evaluation, training and tuning models, and deploying and managing machine learning models in production environments. MLS-C01 Exam also focuses on AWS-specific machine learning services, such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q278-Q283):

NEW QUESTION # 278
A company wants to detect credit card fraud. The company has observed that an average of 2% of credit card transactions are fraudulent. A data scientist trains a classifier on a year's worth of credit card transaction data.
The classifier needs to identify the fraudulent transactions. The company wants to accurately capture as many fraudulent transactions as possible.
Which metrics should the data scientist use to optimize the classifier? (Select TWO.)

A. Accuracy
B. Fl score
C. True positive rate
D. False positive rate
E. Specificity

Answer: B,C

Explanation:
The F1 score is a measure of the harmonic mean of precision and recall, which are both important for fraud detection. Precision is the ratio of true positives to all predicted positives, and recall is the ratio of true positives to all actual positives. A high F1 score indicates that the classifier can correctly identify fraudulent transactions and avoid false negatives. The true positive rate is another name for recall, and it measures the proportion of fraudulent transactions that are correctly detected by the classifier. A high true positive rate means that the classifier can capture as many fraudulent transactions as possible.
References:
* Fraud Detection Using Machine Learning | Implementations | AWS Solutions
* Detect fraudulent transactions using machine learning with Amazon SageMaker | AWS Machine Learning Blog
* 1. Introduction - Reproducible Machine Learning for Credit Card Fraud Detection

NEW QUESTION # 279
A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and
999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set However, the prediction results on a test dataset were not satisfactory.
Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

A. indicate a copy of the samples in the test database in the training dataset
B. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
C. Add more deep trees to the random forest to enable the model to learn more features.
D. Change the cost function so that false positives have a higher impact on the cost value than false negatives
E. Change the cost function so that false negatives have a higher impact on the cost value than false positives

Answer: B,E

Explanation:
Explanation
The Data Science team is facing a problem of imbalanced data, where the positive class (paid users) is much less frequent than the negative class (non-paid users). This can cause the random forest model to be biased towards the majority class and have poor performance on the minority class. To mitigate this issue, the Data Science team can try the following approaches:
C: Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data. This is a technique called data augmentation, which can help increase the size and diversity of the training data for the minority class. This can help the random forest model learn more features and patterns from the positive class and reduce the imbalance ratio.
D: Change the cost function so that false negatives have a higher impact on the cost value than false positives. This is a technique called cost-sensitive learning, which can assign different weights or costs to different classes or errors. By assigning a higher cost to false negatives (predicting non-paid when the user is actually paid), the random forest model can be more sensitive to the minority class and try to minimize the misclassification of the positive class.
References:
Bagging and Random Forest for Imbalanced Classification
Surviving in a Random Forest with Imbalanced Datasets
machine learning - random forest for imbalanced data? - Cross Validated Biased Random Forest For Dealing With the Class Imbalance Problem

NEW QUESTION # 280
A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively.
How should the Specialist address this issue and what is the reason behind it?

A. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
B. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
C. The learning rate should be increased because the optimization process was trapped at a local minimum.
D. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.

Answer: B

Explanation:
Explanation/Reference: https://www.tensorflow.org/tutorials/keras/overfit_and_underfit

NEW QUESTION # 281
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?

A. Bundle the NVIDIA drivers with the Docker image.
B. Build the Docker container to be NVIDIA-Docker compatible.
C. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body
D. Organize the Docker container's file structure to execute on GPU instances.

Answer: B

Explanation:
To leverage the NVIDIA GPUs on Amazon EC2 P3 instances for training a custom ResNet model using Amazon SageMaker, the Machine Learning Specialist needs to build the Docker container to be NVIDIA- Docker compatible. NVIDIA-Docker is a tool that enables GPU-accelerated containers to run on Docker.
NVIDIA-Docker can automatically configure the Docker container with the necessary drivers, libraries, and environment variables to access the NVIDIA GPUs. NVIDIA-Docker can also isolate the GPU resources and ensure that each container has exclusive access to a GPU.
To build a Docker container that is NVIDIA-Docker compatible, the Machine Learning Specialist needs to follow these steps:
* Install the NVIDIA Container Toolkit on the host machine that runs Docker. This toolkit includes the NVIDIA Container Runtime, which is a modified version of the Docker runtime that supports GPU hardware.
* Use the base image provided by NVIDIA as the first line of the Dockerfile. The base image contains the NVIDIA drivers and CUDA toolkit that are required for GPU-accelerated applications. The base image can be specified as FROM nvcr.io/nvidia/cuda:tag, where tag is the version of CUDA and the operating system.
* Install the required dependencies and frameworks for the ResNet model, such as PyTorch, torchvision, etc., in the Dockerfile.
* Copy the ResNet model code and any other necessary files to the Docker container in the Dockerfile.
* Build the Docker image using the docker build command.
* Push the Docker image to a repository, such as Amazon Elastic Container Registry (Amazon ECR), using the docker push command.
* Specify the Docker image URI and the instance type (ml.p3.xlarge) in the Amazon SageMaker CreateTrainingJob request body.
The other options are not valid or sufficient for building a Docker container that can leverage the NVIDIA GPUs on Amazon EC2 P3 instances. Bundling the NVIDIA drivers with the Docker image is not a good option, as it can cause driver conflicts and compatibility issues with the host machine and the NVIDIA GPUs.
Organizing the Docker container's file structure to execute on GPU instances is not a good option, as it does not ensure that the Docker container can access the NVIDIA GPUs and the CUDA toolkit. Setting the GPU flag in the Amazon SageMaker CreateTrainingJob request body is not a good option, as it does not apply to custom Docker containers, but only to built-in algorithms and frameworks that support GPU instances.

NEW QUESTION # 282
A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot Instances in the EMR cluster.
Which nodes should the Specialist launch on Spot Instances?

A. Both core and task nodes
B. Any of the core nodes
C. Master node
D. Any of the task nodes

Answer: C

NEW QUESTION # 283
......

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Exam Dumps Amazon MLS-C01 Free, Test MLS-C01 Simulator Fee

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q278-Q283):

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