Joe Miller Joe Miller
0 Course Enrolled โข 0 Course CompletedBiography
ๅ ่ฒปไธ่ผMLS-C01ๆฐ็้กๅบซไธ็ท & AWS Certified Machine Learning - Specialty่่ฉฆๅคง็ถฑ
P.S. NewDumpsๅจGoogle Driveไธๅไบซไบๅ ่ฒป็ใๆๆฐ็MLS-C01่่ฉฆ้กๅบซ๏ผhttps://drive.google.com/open?id=1K8pp8mlvlRHmK81XbZWDm_Q566UFo8_-
Amazon็MLS-C01็่่ฉฆ่ช่ญๅฐๆฏไฝITไบบๅฃซไพ่ชช้ฝๆฏ้ๅธธ้่ฆ็๏ผๅช่ฆๅพๅฐ้ๅ่ช่ญไฝ ไธๅฎไธๅ่ขซ่ทๅ ดๆทๆฑฐ๏ผไธฆไธไฝ ๅฐๆ่ขซๅ่ท๏ผๅ ่ชใๆไบ้ไบ็พๅฏฆ็ๆฑ่ฅฟ๏ผไฝ ๅฐๅพๅฐไฝ ๆณ่ฆ็ไธๅ๏ผๆไบบ่ชช๏ผ้้ไบAmazon็MLS-C01็่่ฉฆ่ช่ญๅฐฑ็ญๆผ่ตฐๅไบๆๅ๏ผๆฒ้ฏ๏ผ้ๆฏ็็๏ผไฝ ๆไบไฝ ๆณ่ฆ็ไธๅๅฐฑๆฏๆๅ็่กจ็พไนไธใNewDumps็ Amazon็MLS-C01็่้ก่ณๆๆฏไฝ ๅๆๅ็ๆบๆณ๏ผๆไบ้ๅๅน่จ่ณๆ๏ผๅชๆๅ ๅฟซไฝ ๅๆๅ็ๆญฅไผ๏ผ่ฎไฝ ๅๆๅ็ๆดๆ่ชไฟก๏ผไนๆฏไฟ่ญ่ฎไฝ ๅๆๅ็็ ็ขผใ
่ฆๅๅพAWS Certified Machine Learning-Specialty่่ฉฆ่ณๆ ผ๏ผๅ้ธไบบๅฟ ้ ๅจAWSไธ้็ผๆฉๅจๅญธ็ฟๆจกๅๆๆ่ณๅฐไธๅนด็็ถ้ฉ๏ผไธฆไธๅฟ ้ ๅฐๆธๆๅๆใๆธๆๅๅฒๅๆธๆ่็็AWSๆๅๆๆทฑๅ ฅ็ไบ่งฃใ่่ฉฆ็ฑ65ๅๅค้ธๅๅค้ฟๆๅ้ก็ตๆ๏ผๅฟ ้ ๅจ180ๅ้ๅ งๅฎๆใ่ฆ้้่่ฉฆ๏ผๅ้ธไบบๅฟ ้ ๅจ่่ฉฆไธญๅพๅฐ่ณๅฐ72๏ผ ็ๅๆธใ้้่่ฉฆๅพ๏ผๅ้ธไบบๅฐ็ฒๅพAWS Certified Machine Learning-Specialty่ช่ญ๏ผ่ฉฒ่ช่ญๆๆๆ็บไธๅนดใๆญค่ช่ญๅจๅ จ็่ๅๅ งๅพๅฐ่ชๅฏ๏ผไธฆๅฑ็คบไบๅไบบๅจAWSๅนณๅฐไธๆฉๅจๅญธ็ฟ้ ๅ็ๅฐๆฅญ็ฅ่ญใ
>> MLS-C01ๆฐ็้กๅบซไธ็ท <<
MLS-C01ๆฐ็้กๅบซไธ็ท๏ผAmazon่ช่ญMLS-C01่่ฉฆๅคง็ถฑ
็ถไฝ ๆบๅMLS-C01่่ฉฆ็ๆๅ๏ผ็ฒ็ฎๅฐๅญธ็ฟ่่่ฉฆ็ธ้็็ฅ่ญๆฏๅพไธ็ๆณ็ๅญธ็ฟๆนๆณใๅ ถๅฏฆๆณ่ฆ้้่่ฉฆๆฏๆ็ซ ้็ใๅฆๆไฝ ไฝฟ็จไบๅฅฝ็ๅทฅๅ ท๏ผไธๅ ๅฏไปฅ็ฏ็ๅพๅค็ๆ้๏ผ้่ฝๅพๅฐ่ผ้ฌ้้่่ฉฆ็ไฟ่ญใๅฆๆไฝ ๆณ้ฎไปไนๅทฅๅ ท๏ผ้ฃๅฝ็ถๆฏNewDumps็MLS-C01่ๅค้กไบใ
ๆๆฐ็ AWS Certified Specialty MLS-C01 ๅ ่ฒป่่ฉฆ็้ก (Q217-Q222):
ๅ้ก #217
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?
- A. The precision of the model is 86%, which is greater than the accuracy of the model.
- B. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
- C. The precision of the model is 86%, which is less than the accuracy of the model.
- D. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
็ญๆก๏ผD
ย
ๅ้ก #218
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. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
- 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. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
็ญๆก๏ผA
ย
ๅ้ก #219
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 on a P3dn 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 as a batch transform job on an M5 instance.
็ญๆก๏ผC
่งฃ้ก่ชชๆ๏ผ
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
ย
ๅ้ก #220
A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?
- A. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.
- B. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
- C. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset
- D. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
็ญๆก๏ผD
่งฃ้ก่ชชๆ๏ผ
Explanation
Pipe input mode is a feature of Amazon SageMaker that allows streaming large datasets from Amazon S3 directly to the training algorithm without downloading them to the local disk. This reduces the startup time, disk space, and cost of training jobs. Pipe input mode is supported by most of the built-in algorithms and can also be used with custom training algorithms. To use Pipe input mode, the data needs to be in a binary format such as protobuf recordIO or TFRecord. The training code needs to use the PipeModeDataset class to read the data from the named pipe provided by SageMaker. To verify that the training code and the model parameters are working as expected, it is recommended to train locally on a smaller subset of the data before launching a full-scale training job on SageMaker. This approach is faster and more efficient than the other options, which involve either downloading the full dataset to an EC2 instance or using AWS Glue, which is not designed for training machine learning models. References:
Using Pipe input mode for Amazon SageMaker algorithms
Using Pipe Mode with Your Own Algorithms
PipeModeDataset Class
ย
ๅ้ก #221
A bank wants to launch a low-rate credit promotion. The bank is located in a town that recently experienced economic hardship. Only some of the bank's customers were affected by the crisis, so the bank's credit team must identify which customers to target with the promotion. However, the credit team wants to make sure that loyal customers' full credit history is considered when the decision is made.
The bank's data science team developed a model that classifies account transactions and understands credit eligibility. The data science team used the XGBoost algorithm to train the model. The team used 7 years of bank transaction historical data for training and hyperparameter tuning over the course of several days.
The accuracy of the model is sufficient, but the credit team is struggling to explain accurately why the model denies credit to some customers. The credit team has almost no skill in data science.
What should the data science team do to address this issue in the MOST operationally efficient manner?
- A. Create an Amazon SageMaker notebook instance. Use the notebook instance and the XGBoost library to locally retrain the model. Use the plot_importance() method in the Python XGBoost interface to create a feature importance chart. Use that chart to explain to the credit team how the features affect the model outcomes.
- B. Use Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Deploy the model at an endpoint. Use Amazon SageMaker Processing to post-analyze the model and create a feature importance explainability chart automatically for the credit team.
- C. Use Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Activate Amazon SageMaker Debugger, and configure it to calculate and collect Shapley values. Create a chart that shows features and SHapley Additive exPlanations (SHAP) values to explain to the credit team how the features affect the model outcomes.
- D. Use Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Deploy the model at an endpoint. Enable Amazon SageMaker Model Monitor to store inferences. Use the inferences to create Shapley values that help explain model behavior. Create a chart that shows features and SHapley Additive exPlanations (SHAP) values to explain to the credit team how the features affect the model outcomes.
็ญๆก๏ผD
่งฃ้ก่ชชๆ๏ผ
Explanation
The best option is to use Amazon SageMaker Studio to rebuild the model and deploy it at an endpoint. Then, use Amazon SageMaker Model Monitor to store inferences and use the inferences to create Shapley values that help explain model behavior. Shapley values are a way of attributing the contribution of each feature to the model output. They can help the credit team understand why the model makes certain decisions and how the features affect the model outcomes. A chart that shows features and SHapley Additive exPlanations (SHAP) values can be created using the SHAP library in Python. This option is the most operationally efficient because it leverages the existing XGBoost training container and the built-in capabilities of Amazon SageMaker Model Monitor and SHAP library. References:
Amazon SageMaker Studio
Amazon SageMaker Model Monitor
SHAP library
ย
ๅ้ก #222
......
ๆๅNewDumps Amazon็MLS-C01่่ฉฆๅน่จ่ณๆๆไพๆๆต่ก็ๅ ฉ็จฎไธ่ผๆ ผๅผ๏ผไธๅๆฏPDF๏ผๅฆไธๅๆฏ่ป้ซ๏ผๅพๅฎนๆไธ่ผ๏ผๆๅNewDumps่ช่ญ็็ขๅๆบๅ็ITๅฐๆฅญไบบๅฃซๅๅคๅ็ๅฐๅฎถๅทฒ็ถๅฏฆ็พไบไปๅ็ๅฏฆ้็ๆดป็ถ้ฉ๏ผ ๅจๅธๅ ดไธๆไพๆๅฅฝ็็ขๅ๏ผไปฅๅฏฆ็พไฝ ็็ฎๆจใ
MLS-C01่่ฉฆๅคง็ถฑ: https://www.newdumpspdf.com/MLS-C01-exam-new-dumps.html
ๅช่ฆๆจไฝฟ็จๆฌ็ซ็้กๅบซๅญธ็ฟ่ณๆๅๅ MLS-C01่่ฉฆๅคง็ถฑ - AWS Certified Machine Learning - Specialty่่ฉฆ๏ผๅฐๆๆ็ๆ้ซๆจ็ๅญธ็ฟๆ็๏ผ้ไฝ่่ฉฆๆๆฌ๏ผๅฆไฝไฝฟ็จMLS-C01ๅ้ก้๏ผAmazon MLS-C01ๆฐ็้กๅบซไธ็ท ไฝ ๆๆณ้้ธๆไธๅ้ๅฐๆง็ๅน่จๅ๏ผๅฆๆไฝ ๆณๅจIT่กๆฅญๆๆๆดๅฅฝ็็ผๅฑ๏ผๆๆ้ซ็ซฏ็ๆ่กๆฐดๆบ๏ผAmazon MLS-C01ๆฏ็ขบไฟไฝ ็ฒๅพๅคขๆณๅทฅไฝ็ๅฏไธ้ธๆ๏ผ็บไบๅฏฆ็พ้ไธๅคขๆณ๏ผ่ถๅฟซ่กๅๅง๏ผAmazon MLS-C01ๆฐ็้กๅบซไธ็ท ๅจ้ไบ็ญ็ดไธญ๏ผไธๅ็็ผๅฑ้ๅพๅฐๆไธๅ็่ทๆฅญ้ๆฑ๏ผNewDumps็่่่ฏ่ตๆไธๅฎ่ฝๅธฎๅฉไฝ ่ทๅพMLS-C01่่ฏ็่ฎค่ฏ่ตๆ ผ๏ผNewDumps MLS-C01 ่่ฉฆๅคง็ถฑๆฏไธๅ็บITไบบๅฃซๅๅ ็ธ้่ช่ญ่่ฉฆๆไพ่ณๆบ็ไพฟๅฉ็ถฒ็ซใ
ๅฆณไปฅ็บๅฆณ็ๅฐๆๅชๆๆๅฃนๅๅ๏ผ็ฎก่ช่็่่ฒๅพๆฏ้ฃ็ไบ๏ผ้ๅฐๅญ้็ๆฏ่ฆๆกๅฟ่ชๅทฑไบ๏ผๅช่ฆๆจไฝฟ็จๆฌ็ซ็้กๅบซๅญธ็ฟ่ณๆๅๅ AWS Certified Machine Learning - Specialty่่ฉฆ๏ผๅฐๆๆ็ๆ้ซๆจ็ๅญธ็ฟๆ็๏ผ้ไฝ่่ฉฆๆๆฌ๏ผๅฆไฝไฝฟ็จMLS-C01ๅ้ก้๏ผไฝ ๆๆณ้้ธๆไธๅ้ๅฐๆง็ๅน่จๅ๏ผ
่ณๆ ผ่่ฉฆไธญ็ๆไฝณMLS-C01ๆฐ็้กๅบซไธ็ทๅ้ ๅ ไพๆๅ๏ผๆ่ฟๆดๆญฃ็Amazon AWS Certified Machine Learning - Specialty
ๅฆๆไฝ ๆณๅจIT่กๆฅญๆๆๆดๅฅฝ็็ผๅฑ๏ผๆๆ้ซ็ซฏ็ๆ่กๆฐดๆบ๏ผAmazon MLS-C01ๆฏ็ขบไฟไฝ ็ฒๅพๅคขๆณๅทฅไฝ็ๅฏไธ้ธๆ๏ผ็บไบๅฏฆ็พ้ไธๅคขๆณ๏ผ่ถๅฟซ่กๅๅง๏ผๅจ้ไบ็ญ็ดไธญ๏ผไธๅ็็ผๅฑ้ๅพๅฐๆไธๅ็่ทๆฅญ้ๆฑใ
- MLS-C01 PDF้กๅบซ ๐ MLS-C01้กๅบซๆดๆฐ่ณ่จ ๐ MLS-C01ๅญธ็ฟ็ญ่จ โ ๅจโ www.pdfexamdumps.com โ็ถฒ็ซไธๆฅๆพโฉ MLS-C01 โช็ๆๆฐ้กๅบซMLS-C01้กๅบซ
- MLS-C01้กๅบซๆดๆฐ่ณ่จ ๐ MLS-C01่่ฉฆๅคง็ถฑ ๐ฏ MLS-C01่ช่ญ่่ฉฆ่งฃๆ ๐ ็ซๅณๅฐ๏ผ www.newdumpspdf.com ๏ผไธๆ็ดขโ MLS-C01 ๏ธโ๏ธไปฅ็ฒๅๅ ่ฒปไธ่ผMLS-C01่่ฉฆๅฟๅพ
- ๆฐ็็MLS-C01้กๅบซไธ็ท - ไธ่ผMLS-C01้กๅบซ - ้้MLS-C01่ช่ญ่่ฉฆ โ โถ www.kaoguti.com โๆฏ็ฒๅ{ MLS-C01 }ๅ ่ฒปไธ่ผ็ๆไฝณ็ถฒ็ซMLS-C01่ช่ญๆๅ
- ๅฟซ้ไธ่ผMLS-C01ๆฐ็้กๅบซไธ็ทๅ่ณๆ ผ่่ฉฆ้ ๅฐ่ ๅๅฏ้ ็MLS-C01่่ฉฆๅคง็ถฑ ๐ { www.newdumpspdf.com }ๆฏ็ฒๅโ MLS-C01 โๅ ่ฒปไธ่ผ็ๆไฝณ็ถฒ็ซMLS-C01่ญ็ ง่่ฉฆ
- MLS-C01่่ฉฆ่ณ่จ ๐ฅซ MLS-C01่ช่ญๆๅ ๐ณ MLS-C01่่ฉฆๅ่็ถ้ฉ โ ่ค่ฃฝ็ถฒๅ๏ผ www.pdfexamdumps.com ๏ผๆ้ไธฆๆ็ดข๏ผ MLS-C01 ๏ผๅ ่ฒปไธ่ผMLS-C01 PDF้กๅบซ
- MLS-C01่่ฉฆๅคง็ถฑ ๐ป MLS-C01่ญ็ ง่่ฉฆ ๐ฆ MLS-C01ๅญธ็ฟ็ญ่จ ๐ช ๅจโถ www.newdumpspdf.com โ็ถฒ็ซไธ่ผๅ ่ฒปโฎ MLS-C01 โฎ้กๅบซๆถ้MLS-C01้กๅบซ
- MLS-C01่่ฉฆๅคง็ถฑ ๐ฒ MLS-C01่ๅค้กๅไบซ โบ MLS-C01้กๅบซ ๐ ้้โ tw.fast2test.com โ่ผ้ฌ็ฒๅโ MLS-C01 โๅ ่ฒปไธ่ผMLS-C01่ๅค้กๅไบซ
- ๆๆ็MLS-C01ๆฐ็้กๅบซไธ็ท๏ผ้ซ่ณช้็่่ฉฆ้กๅบซๅนซๅฉๅฆณ่ผๆพ้้MLS-C01่่ฉฆ ๐ป ๅจโ www.newdumpspdf.com ๐ ฐ็ถฒ็ซไธๆฅๆพใ MLS-C01 ใ็ๆๆฐ้กๅบซMLS-C01้กๅบซ
- ๅฟซ้ไธ่ผMLS-C01ๆฐ็้กๅบซไธ็ทๅ่ณๆ ผ่่ฉฆ้ ๅฐ่ ๅๅฏ้ ็MLS-C01่่ฉฆๅคง็ถฑ ๐ข ่ซๅจโ tw.fast2test.com โ็ถฒ็ซไธๅ ่ฒปไธ่ผโฝ MLS-C01 ๐ขช้กๅบซMLS-C01่่ฉฆๅคง็ถฑ
- MLS-C01้กๅบซไธ่ผ ๐บ MLS-C01่ช่ญๆๅ ๐ผ MLS-C01ๅญธ็ฟ็ญ่จ โฃ โท www.newdumpspdf.com โ็ถฒ็ซๆ็ดขโฝ MLS-C01 ๐ขชไธฆๅ ่ฒปไธ่ผMLS-C01่ญ็ ง่่ฉฆ
- MLS-C01่่ฉฆๅฟๅพ ๐ MLS-C01่ๅค้กๅไบซ โฌ MLS-C01่่ฉฆ่ณ่จ ๐บ ๅ ่ฒปไธ่ผโ MLS-C01 โๅช้ๅจโ www.testpdf.net โไธๆ็ดขMLS-C01่ช่ญ้กๅบซ
- uniway.edu.lk, gs.gocfa.net, motionentrance.edu.np, motionentrance.edu.np, daotao.wisebusiness.edu.vn, eastwest-lms.com, 58laoxiang.com, formazionebusinessschool.sch.ng, motionentrance.edu.np, nurture.unirhythm.in
ๅพGoogle Driveไธญๅ ่ฒปไธ่ผๆๆฐ็NewDumps MLS-C01 PDF็่่ฉฆ้กๅบซ๏ผhttps://drive.google.com/open?id=1K8pp8mlvlRHmK81XbZWDm_Q566UFo8_-