PDF Amazon AWS-Certified-Machine-Learning-Specialty Cram Exam | AWS-Certified-Machine-Learning-Specialty Training Solutions

Our AWS-Certified-Machine-Learning-Specialty guide torrent has gone through strict analysis and summary according to the past exam papers and the popular trend in the industry and are revised and updated. The AWS-Certified-Machine-Learning-Specialty exam questions have simplified the sophisticated notions. The software boosts varied self-learning and self-assessment functions to check the learning results. The software of our AWS-Certified-Machine-Learning-Specialty Test Torrent provides the statistics report function and help the students find the weak links and deal with them. With this version of our AWS-Certified-Machine-Learning-Specialty exam questions, you will be able to pass the exam easily.

Amazon MLS-C01 exam is a certification exam offered by Amazon Web Services (AWS) for individuals who want to demonstrate their expertise in machine learning. AWS-Certified-Machine-Learning-Specialty exam is intended for individuals who have a deep understanding and practical experience in designing and implementing machine learning solutions using AWS services.

Amazon AWS Certified Machine Learning - Specialty exam is a certification program designed to validate an individual's knowledge and skills in the field of machine learning on the Amazon Web Services (AWS) platform. AWS-Certified-Machine-Learning-Specialty exam is intended for individuals who have experience working with data analytics and machine learning, and are looking to expand their knowledge and skills in this domain. AWS-Certified-Machine-Learning-Specialty Exam is designed to test the candidate's knowledge of machine learning concepts, data preparation, feature engineering, model selection, and more specific topics related to machine learning on the AWS platform.

References:

AWS Certified Machine Learning Specialty Exam Reference

>> PDF Amazon AWS-Certified-Machine-Learning-Specialty Cram Exam <<

AWS-Certified-Machine-Learning-Specialty Training Solutions | AWS-Certified-Machine-Learning-Specialty Valid Dump

We are well-known for our wonderful performance on pushing more and more candidates to pass their AWS-Certified-Machine-Learning-Specialty exams and achieve their dreaming certifications. There is no exaggeration to say that with our AWS-Certified-Machine-Learning-Specialty study materials for 20 to 30 hours, you will be ready to pass your AWS-Certified-Machine-Learning-Specialty Exam. Since our AWS-Certified-Machine-Learning-Specialty exam torrent is designed on the purpose to be understood by our customers all over the world, it is compiled into the simplest language to save time and efforts.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q218-Q223):

NEW QUESTION # 218
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training.
The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll). The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?

A. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
B. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.
C. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
D. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.

Answer: C

Explanation:
A VPC endpoint is a logical device that enables private connections between a VPC and supported AWS services. A VPC endpoint can be either a gateway endpoint or an interface endpoint. A gateway endpoint is a gateway that is a target for a specified route in the route table, used for traffic destined to a supported AWS service. An interface endpoint is an elastic network interface with a private IP address that serves as an entry point for traffic destined to a supported service1 In this case, the Machine Learning Specialist can create a gateway endpoint for Amazon S3, which is a supported service for gateway endpoints. A gateway endpoint for Amazon S3 enables the VPC to access Amazon S3 privately, without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection. The traffic between the VPC and Amazon S3 does not leave the Amazon network2 To restrict access to the dataset stored in Amazon S3, the Machine Learning Specialist can apply a bucket access policy that allows access only from the given VPC endpoint and the VPC. A bucket access policy is a resource-based policy that defines who can access a bucket and what actions they can perform. A bucket access policy can use various conditions to control access, such as the source IP address, the source VPC, the source VPC endpoint, etc. In this case, the Machine Learning Specialist can use the aws:sourceVpce condition to specify the ID of the VPC endpoint, and the aws:sourceVpc condition to specify the ID of the VPC. This way, only the requests that originate from the VPC endpoint or the VPC can access the bucket that contains the dataset34 The other options are not valid or secure ways to satisfy the requirements. Creating a VPC endpoint and applying a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance is not a good option, as it does not restrict access to the VPC. An Amazon EC2 instance is a virtual server that runs in the AWS cloud. An Amazon EC2 instance can have a public IP address or a private IP address, depending on the network configuration. Allowing access from an Amazon EC2 instance does not guarantee that the instance is in the same VPC as the VPC endpoint, and may expose the dataset to unauthorized access. Creating a VPC endpoint and using Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance is not a good option, as it does not restrict access to the VPC. NACLs are stateless firewalls that can control inbound and outbound traffic at the subnet level. NACLs can use rules to allow or deny traffic based on the protocol, port, and source or destination IP address. However, NACLs do not support VPC endpoints as a source or destination, and cannot filter traffic based on the VPC endpoint ID or the VPC ID. Therefore, using NACLs does not guarantee that the traffic is from the VPC endpoint or the VPC, and may expose the dataset to unauthorized access. Creating a VPC endpoint and using security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance is not a good option, as it does not restrict access to the VPC. Security groups are stateful firewalls that can control inbound and outbound traffic at the instance level. Security groups can use rules to allow or deny traffic based on the protocol, port, and source or destination. However, security groups do not support VPC endpoints as a source or destination, and cannot filter traffic based on the VPC endpoint ID or the VPC ID. Therefore, using security groups does not guarantee that the traffic is from the VPC endpoint or the VPC, and may expose the dataset to unauthorized access.

NEW QUESTION # 219
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. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
B. indicate a copy of the samples in the test database in the training dataset
C. Change the cost function so that false negatives have a higher impact on the cost value than false positives
D. Add more deep trees to the random forest to enable the model to learn more features.
E. Change the cost function so that false positives have a higher impact on the cost value than false negatives

Answer: A,C

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 # 220
An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored for offline analysis.
What approach would be the MOST effective to perform near-real time defect detection?

A. Use Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry out Apache Spark ML k-means clustering to determine anomalies.
B. Use AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from within AWS IoT Analytics to carry out analysis for anomalies.
C. Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further
D. Use Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random Cut Forest (RCF) algorithm to determine anomalies.

Answer: A

Explanation:
analysis.

NEW QUESTION # 221
A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test"?

A. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced
B. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker
C. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.
D. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker

Answer: B

Explanation:
Amazon CloudWatch is a service that can monitor and collect various metrics and logs from AWS resources, such as Amazon SageMaker. Amazon CloudWatch can also generate dashboards to create a single view for the metrics and logs that are of interest. By using Amazon CloudWatch, the Machine Learning Specialist can review the latency, memory utilization, and CPU utilization during the load test, as these are some of the metrics that are outputted by Amazon SageMaker. The Specialist can create a custom dashboard that displays these metrics in different widgets, such as graphs, tables, or text. The dashboard can also be configured to refresh automatically and show the latest data as the load test is running. This approach will allow the Specialist to monitor the performance and resource utilization of the model variant and adjust the Auto Scaling configuration accordingly.
[Monitoring Amazon SageMaker with Amazon CloudWatch - Amazon SageMaker]
[Using Amazon CloudWatch Dashboards - Amazon CloudWatch]
[Create a CloudWatch Dashboard - Amazon CloudWatch]

NEW QUESTION # 222
An agricultural company is interested in using machine learning to detect specific types of weeds in a 100-acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10 * 10 grids. The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks.
The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints. The model will perform real-time inferencing using the images captured by the cameras.
Which approach should a Machine Learning Specialist take to obtain accurate predictions?

A. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.
B. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
C. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
D. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.

Answer: D

NEW QUESTION # 223
......

It is universally acknowledged that the pass rate is the most persuasive evidence to prove how useful and effective a kind of AWS-Certified-Machine-Learning-Specialty practice test is. In terms of our training materials, the pass rate is one of the aspects that we take so much pride in because according to the statistics from the feedbacks of all of our customers, under the guidance of our AWS-Certified-Machine-Learning-Specialty Preparation materials the pass rate among our customers has reached as high as 98% to 100%, which marks the highest pass rate in the field. Just feel rest assured to buy our AWS-Certified-Machine-Learning-Specialty study guide, which definitely will be the best choice for you.

AWS-Certified-Machine-Learning-Specialty Training Solutions: https://www.prep4away.com/Amazon-certification/braindumps.AWS-Certified-Machine-Learning-Specialty.ete.file.html