AWS-Certified-Machine-Learning-Specialty최신시험공부자료 - AWS-Certified-Machine-Learning-Specialty최신인증시험기출문제

Itcertkr에서 제공되는Amazon AWS-Certified-Machine-Learning-Specialty인증시험덤프의 문제와 답은 실제시험의 문제와 답과 아주 유사합니다. 아니 거이 같습니다. 우리Itcertkr의 덤프를 사용한다면 우리는 일년무료 업뎃서비스를 제공하고 또 100%통과 율을 장담합니다. 만약 여러분이 시험에서 떨어졌다면 우리는 덤프비용전액을 환불해드립니다.

이 시험은 기계 학습 알고리즘 및 프레임 워크를 깊이 이해하는 사람들뿐만 아니라 Amazon Sagemaker, Amazon S3, Amazon EC2 및 Amazon EMR과 같은 AWS 서비스에 대한 경험을 제공하는 사람들을위한 것입니다. 응시자는 또한 Python 및 R과 같은 프로그래밍 언어에 대한 경험과 데이터 전처리, 기능 엔지니어링 및 모델 평가 경험이 있어야합니다.

AWS Certified Machine Learning - Specialty 시험은 65개의 객관식 및 다중응답 문제로 이루어져 있으며, 시험 시간은 3시간입니다. 이 시험은 AWS 플랫폼에서 머신러닝 솔루션을 설계, 구현, 배포 및 유지 관리하는 능력을 시험합니다. 시험 응시 자격을 얻으려면, AWS 플랫폼에서 머신러닝 솔루션을 개발 및 유지 관리한 경험이 최소 1년 이상이어야 합니다. 시험에 성공하면, AWS Certified Machine Learning - Specialty 자격증을 받게 되며 이는 전 세계적으로 인정되고 AWS 플랫폼에서 머신러닝 분야의 전문 지식을 증명합니다.

>> AWS-Certified-Machine-Learning-Specialty최신 시험 공부자료 <<

AWS-Certified-Machine-Learning-Specialty최신 시험 공부자료최신버전 시험공부자료

Itcertkr를 검색을 통해 클릭하게된 지금 이 순간 IT인증자격증취득Amazon AWS-Certified-Machine-Learning-Specialty시험은 더는 힘든 일이 아닙니다. 다른 분들이Amazon AWS-Certified-Machine-Learning-Specialty시험준비로 수없는 고민을 할때 고객님은 저희 Amazon AWS-Certified-Machine-Learning-Specialty덤프로 제일 빠른 시일내에 시험을 패스하여 자격증을 손에 넣을수 있습니다.

AWS 인증 머신 러닝 - 전문 인증 시험을 받으려면 응시자는 AWS를 사용하여 기계 학습 솔루션을 개발하고 유지 관리하는 데 최소 1 년의 경험이 있어야합니다. 또한 AWS Lambda, AWS S3, AWS EC2 및 AWS CloudFormation과 같은 AWS 서비스를 잘 이해해야합니다.

최신 AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 무료샘플문제 (Q265-Q270):

질문 # 265
A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

A. Residual plots
B. Root Mean Square Error (RMSE)
C. Area under the curve
D. Confusion matrix

정답：A

설명：
Residual plots are a model evaluation technique that can be used to understand whether a regression model is more frequently overestimating or underestimating the target. Residual plots are graphs that plot the residuals (the difference between the actual and predicted values) against the predicted values or other variables.
Residual plots can help the Machine Learning Specialist to identify the patterns and trends in the residuals, such as the direction, shape, and distribution. Residual plots can also reveal the presence of outliers, heteroscedasticity, non-linearity, or other problems in the model12 To determine whether the model is overestimating or underestimating the target, the Machine Learning Specialist can use a residual plot that plots the residuals against the predicted values. This type of residual plot is also known as a prediction error plot. A prediction error plot can show the magnitude and direction of the errors made by the model. If the model is overestimating the target, the residuals will be negative, and the points will be below the zero line. If the model is underestimating the target, the residuals will be positive, and the points will be above the zero line. If the model is accurate, the residuals will be close to zero, and the points will be scattered around the zero line. A prediction error plot can also show the variance and bias of the model. If the model has high variance, the residuals will have a large spread, and the points will be far from the zero line. If the model has high bias, the residuals will have a systematic pattern, such as a curve or a slope, and the points will not be randomly distributed around the zero line. A prediction error plot can help the Machine Learning Specialist to optimize the model by adjusting the complexity, features, or parameters of the model34 The other options are not valid or suitable for determining whether the model is overestimating or underestimating the target. Root Mean Square Error (RMSE) is a model evaluation metric that measures the average magnitude of the errors made by the model. RMSE is the square root of the mean of the squared residuals. RMSE can indicate the overall accuracy and performance of the model, but it cannot show the direction or distribution of the errors. RMSE can also be influenced by outliers or extreme values, and it may not be comparable across different models or datasets5 Area under the curve (AUC) is a model evaluation metric that measures the ability of the model to distinguish between the positive and negative classes. AUC is the area under the receiver operating characteristic (ROC) curve, which plots the true positive rate against the false positive rate for various classification thresholds. AUC can indicate the overall quality and performance of the model, but it is only applicable for binary classification models, not regression models. AUC cannot show the magnitude or direction of the errors made by the model. Confusion matrix is a model evaluation technique that summarizes the number of correct and incorrect predictions made by the model for each class.
A confusion matrix is a table that shows the counts of true positives, false positives, true negatives, and false negatives for each class. A confusion matrix can indicate the accuracy, precision, recall, and F1-score of the model for each class, but it is only applicable for classification models, not regression models. A confusion matrix cannot show the magnitude or direction of the errors made by the model.

질문 # 266
A data scientist stores financial datasets in Amazon S3. The data scientist uses Amazon Athena to query the datasets by using SQL.
The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model. The data scientist wants to obtain inferences from the model at the SageMaker endpoint However, when the data .... ntist attempts to invoke the SageMaker endpoint, the data scientist receives SOL statement failures The data scientist's 1AM user is currently unable to invoke the SageMaker endpoint Which combination of actions will give the data scientist's 1AM user the ability to invoke the SageMaker endpoint? (Select THREE.)

A. Perform a user remapping in SageMaker to map the 1AM user to another 1AM user that is on the hosted endpoint.
B. Attach the AmazonAthenaFullAccess AWS managed policy to the user identity.
C. Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemakerGetRecord action.
D. Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemaker: lnvokeEndpoint action,
E. Include the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in the Athena SQL query.
F. Include an inline policy for the data scientist's 1AM user that allows SageMaker to read S3 objects

정답：D,E,F

설명：
Explanation
The correct combination of actions to enable the data scientist's IAM user to invoke the SageMaker endpoint is B, C, and E, because they ensure that the IAM user has the necessary permissions, access, and syntax to query the ML model from Athena. These actions have the following benefits:
B: Including a policy statement for the IAM user that allows the sagemaker:InvokeEndpoint action grants the IAM user the permission to call the SageMaker Runtime InvokeEndpoint API, which is used to get inferences from the model hosted at the endpoint1.
C: Including an inline policy for the IAM user that allows SageMaker to read S3 objects enables the IAM user to access the data stored in S3, which is the source of the Athena queries2.
E: Including the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in the Athena SQL query allows the IAM user to invoke the ML model as an external function from Athena, which is a feature that enables querying ML models from SQL statements3.
The other options are not correct or necessary, because they have the following drawbacks:
A: Attaching the AmazonAthenaFullAccess AWS managed policy to the user identity is not sufficient, because it does not grant the IAM user the permission to invoke the SageMaker endpoint, which is required to query the ML model4.
D: Including a policy statement for the IAM user that allows the IAM user to perform the sagemaker:GetRecord action is not relevant, because this action is used to retrieve a single record from a feature group, which is not the case in this scenario5.
F: Performing a user remapping in SageMaker to map the IAM user to another IAM user that is on the hosted endpoint is not applicable, because this feature is only available for multi-model endpoints, which are not used in this scenario.
References:
1: InvokeEndpoint - Amazon SageMaker
2: Querying Data in Amazon S3 from Amazon Athena - Amazon Athena
3: Querying machine learning models from Amazon Athena using Amazon SageMaker | AWS Machine Learning Blog
4: AmazonAthenaFullAccess - AWS Identity and Access Management
5: GetRecord - Amazon SageMaker Feature Store Runtime
6: [Invoke a Multi-Model Endpoint - Amazon SageMaker]

질문 # 267
A logistics company needs a forecast model to predict next month's inventory requirements for a single item in
10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor.
The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.
Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)

A. Set PerformAutoML to true.
B. Set ForecastFrequency to W for weekly.
C. Set FeaturizationMethodName to filling.
D. Set ForecastHorizon to 4.
E. Set PerformHPO to true.

정답：A,E

설명：
Explanation
The MAPE of the predictor could be improved by making the following changes to the CreatePredictor API call:
Set PerformAutoML to true. This will allow Amazon Forecast to automatically evaluate different algorithms and choose the one that minimizes the objective function, which is the mean of the weighted losses over the forecast types. By default, these are the p10, p50, and p90 quantile losses1. This option can help find a better algorithm than DeepAR+ for the given data.
Set PerformHPO to true. This will enable hyperparameter optimization (HPO), which is the process of finding the optimal values for the algorithm-specific parameters that affect the quality of the forecasts. HPO can improve the accuracy of the predictor by tuning the hyperparameters based on the training data2.
The other options are not likely to improve the MAPE of the predictor. Setting ForecastHorizon to 4 will reduce the number of time steps that the model predicts, which may not match the business requirement of predicting next month's inventory. Setting ForecastFrequency to W for weekly will change the granularity of the forecasts, which may not be appropriate for the monthly data. Setting FeaturizationMethodName to filling will not have any effect, since there is no missing data in the dataset.
References:
CreatePredictor - Amazon Forecast
HPOConfig - Amazon Forecast

질문 # 268
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS Which approach should the Specialist use for training a model using that data?

A. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
B. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
C. Write a direct connection to the SQL database within the notebook and pull data in
D. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

정답：A

설명：
Explanation
Pushing the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and providing the S3 location within the notebook is the best approach for training a model using the data stored in Amazon RDS. This is because Amazon SageMaker can directly access data from Amazon S3 and train models on it.
AWS Data Pipeline is a service that can automate the movement and transformation of data between different AWS services. It can also use Amazon RDS as a data source and Amazon S3 as a data destination. This way, the data can be transferred efficiently and securely without writing any code within the notebook. References:
Amazon SageMaker
AWS Data Pipeline

질문 # 269
A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?

A. Redeploy the model as a batch transform job on an M5 instance.
B. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.
C. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
D. Redeploy the model on a P3dn instance.

정답：C

설명：
The best way to ensure that provisioned resources are being utilized effectively is to redeploy the model on an M5 instance and attach Amazon Elastic Inference to the instance. Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. By using Amazon Elastic Inference, you can choose the instance type that is best suited to the overall CPU and memory needs of your application, and then separately configure the amount of inference acceleration that you need with no code changes. This way, you can avoid wasting GPU resources and pay only for what you use.
Option A is incorrect because a batch transform job is not suitable for real-time predictions. Batch transform is a high-performance and cost-effective feature for generating inferences using your trained models. Batch transform manages all of the compute resources required to get inferences. Batch transform is ideal for scenarios where you're working with large batches of data, don't need sub-second latency, or need to process data that is stored in Amazon S3.
Option C is incorrect because redeploying the model on a P3dn instance would not improve the resource utilization. P3dn instances are designed for distributed machine learning and high performance computing applications that need high network throughput and packet rate performance. They are not optimized for inference workloads.
Option D is incorrect because deploying the model onto an Amazon ECS cluster using a P3 instance would not ensure that provisioned resources are being utilized effectively. Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. However, using Amazon ECS would not address the issue of underutilized GPU resources. In fact, it might introduce additional overhead and complexity in managing the cluster.
References:
Amazon Elastic Inference - Amazon SageMaker
Batch Transform - Amazon SageMaker
Amazon EC2 P3 Instances
Amazon EC2 P3dn Instances
Amazon Elastic Container Service

질문 # 270
......

AWS-Certified-Machine-Learning-Specialty최신 인증시험 기출문제: https://www.itcertkr.com/AWS-Certified-Machine-Learning-Specialty_exam.html

Lou Bell Lou Bell

سيرة شخصية

AWS-Certified-Machine-Learning-Specialty최신시험공부자료 - AWS-Certified-Machine-Learning-Specialty최신인증시험기출문제

AWS-Certified-Machine-Learning-Specialty최신 시험 공부자료최신버전 시험공부자료

최신 AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 무료샘플문제 (Q265-Q270):

روابط مهمة

النشرة الاخبارية