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To take the Amazon MLS-C01 exam, candidates must first pass the AWS Certified Cloud Practitioner or AWS Certified Solutions Architect Associate exam. AWS-Certified-Machine-Learning-Specialty Exam consists of 65 multiple-choice and multiple-response questions and must be completed in 180 minutes. AWS-Certified-Machine-Learning-Specialty exam is available in several languages, including English, Japanese, Korean, and Simplified Chinese. Upon passing the exam, candidates will receive an AWS Certified Machine Learning – Specialty certification, which is valid for three years. AWS Certified Machine Learning - Specialty certification demonstrates to employers and clients that the individual has the skills and knowledge needed to design, implement, and maintain machine learning solutions on the AWS platform.
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NEW QUESTION # 234
A credit card company wants to identify fraudulent transactions in real time. A data scientist builds a machine learning model for this purpose. The transactional data is captured and stored in Amazon S3. The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative). The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker.
The model produces the following results:
* True positive rate (TPR): 0.700
* False negative rate (FNR): 0.300
* True negative rate (TNR): 0.977
* False positive rate (FPR): 0.023
* Overall accuracy: 0.949
Which solution should the data scientist use to improve the performance of the model?
Answer: A
Explanation:
The solution that the data scientist should use to improve the performance of the model is to apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset, and retrain the model with the updated training data. This solution can address the problem of class imbalance in the dataset, which can affect the model's ability to learn from the rare but important positive class (fraud).
Class imbalance is a common issue in machine learning, especially for classification tasks. It occurs when one class (usually the positive or target class) is significantly underrepresented in the dataset compared to the other class (usually the negative or non-target class). For example, in the credit card fraud detection problem, the positive class (fraud) is much less frequent than the negative class (fair transactions). This can cause the model to be biased towards the majority class, and fail to capture the characteristics and patterns of the minority class. As a result, the model may have a high overall accuracy, but a low recall or true positive rate for the minority class, which means it misses many fraudulent transactions.
SMOTE is a technique that can help mitigate the class imbalance problem by generating synthetic samples for the minority class. SMOTE works by finding the k-nearest neighbors of each minority class instance, and randomly creating new instances along the line segments connecting them. This way, SMOTE can increase the number and diversity of the minority class instances, without duplicating or losing any information. By applying SMOTE on the minority class in the training dataset, the data scientist can balance the classes and improve the model's performance on the positive class1.
The other options are either ineffective or counterproductive. Applying SMOTE on the majority class would not balance the classes, but increase the imbalance and the size of the dataset. Undersampling the minority class would reduce the number of instances available for the model to learn from, and potentially lose some important information. Oversampling the majority class would also increase the imbalance and the size of the dataset, and introduce redundancy and overfitting.
1: SMOTE for Imbalanced Classification with Python - Machine Learning Mastery
NEW QUESTION # 235
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?
Answer: C
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 # 236
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
* Real-time analytics
* Interactive analytics of historical data
* Clickstream analytics
* Product recommendations
Which services should the Specialist use?
Answer: D
Explanation:
The best services to use for building a data ingestion solution for the company's Amazon S3-based data lake are:
* AWS Glue as the data catalog: AWS Glue is a fully managed extract, transform, and load (ETL) service that can discover, crawl, and catalog data from various sources and formats, and make it available for analysis. AWS Glue can also generate ETL code in Python or Scala to transform, enrich, and join data using AWS Glue Data Catalog as the metadata repository. AWS Glue Data Catalog is a central metadata store that integrates with Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum, allowing users to create a unified view of their data across various sources and formats.
* Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights: Amazon Kinesis Data Streams is a service that enables users to collect, process, and analyze real-time streaming data at any scale. Users can create data streams that can capture data from various sources, such as web and mobile applications, IoT devices, and social media platforms. Amazon Kinesis Data Analytics is a service that allows users to analyze streaming data using standard SQL queries or Apache Flink applications. Users can create real-time dashboards, metrics, and alerts based on the streaming data analysis results.
* Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics: Amazon Kinesis Data Firehose is a service that enables users to load streaming data into data lakes, data stores, and analytics services. Users can configure Kinesis Data Firehose to automatically deliver data to various destinations, such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and third-party solutions. For clickstream analytics, users can use Kinesis Data Firehose to deliver data to Amazon OpenSearch Service, a fully managed service that offers search and analytics capabilities for log data.
Users can use Amazon OpenSearch Service to perform interactive analysis and visualization of clickstream data using Kibana, an open-source tool that is integrated with Amazon OpenSearch Service.
* Amazon EMR to generate personalized product recommendations: Amazon EMR is a service that enables users to run distributed data processing frameworks, such as Apache Spark, Apache Hadoop, and Apache Hive, on scalable clusters of EC2 instances. Users can use Amazon EMR to perform advanced analytics, such as machine learning, on large and complex datasets stored in Amazon S3 or other sources. For product recommendations, users can use Amazon EMR to run Spark MLlib, a library that provides scalable machine learning algorithms, such as collaborative filtering, to generate personalized recommendations based on user behavior and preferences.
References:
* AWS Glue - Fully Managed ETL Service
* Amazon Kinesis - Data Streaming Service
* Amazon OpenSearch Service - Managed OpenSearch Service
* Amazon EMR - Managed Hadoop Framework
NEW QUESTION # 237
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
Answer: D
Explanation:
Area Under the ROC Curve (AUC) is a metric that measures the performance of a binary classifier across all possible thresholds. It is also known as the probability that a randomly chosen positive example will be ranked higher than a randomly chosen negative example by the classifier. AUC is a good metric to compare different classification models because it is independent of the class distribution and the decision threshold. It also captures both the sensitivity (true positive rate) and the specificity (true negative rate) of the model.
References:
* AWS Machine Learning Specialty Exam Guide
* AWS Machine Learning Specialty Sample Questions
NEW QUESTION # 238
A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.
Which services are integrated with Amazon SageMaker to track this information? (Select TWO.)
Answer: B,E
NEW QUESTION # 239
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