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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) certification exam is a specialized exam designed for individuals who want to validate their ability to design, implement, deploy, and maintain machine learning (ML) solutions on the Amazon Web Services (AWS) platform. AWS Certified Machine Learning - Specialty certification is ideal for professionals who have experience in ML and want to showcase their skills and knowledge in this area. AWS-Certified-Machine-Learning-Specialty Exam is intended for individuals who have a deep understanding of ML frameworks, algorithms, and AWS services, and want to demonstrate their expertise to potential employers and clients.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q106-Q111):

NEW QUESTION # 106
A company plans to build a custom natural language processing (NLP) model to classify and prioritize user feedback. The company hosts the data and all machine learning (ML) infrastructure in the AWS Cloud. The ML team works from the company's office, which has an IPsec VPN connection to one VPC in the AWS Cloud.
The company has set both the enableDnsHostnames attribute and the enableDnsSupport attribute of the VPC to true. The company's DNS resolvers point to the VPC DNS. The company does not allow the ML team to access Amazon SageMaker notebooks through connections that use the public internet. The connection must stay within a private network and within the AWS internal network.
Which solution will meet these requirements with the LEAST development effort?

A. Create a bastion host by using Amazon EC2 in a public subnet within the VPC. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.
B. Create a VPC interface endpoint for the SageMaker notebook in the VPC. Access the notebook through a VPN connection and the VPC endpoint.
C. Create a NAT gateway in the VPC. Access the SageMaker notebook HTTPS endpoint through a VPN connection and the NAT gateway.
D. Create a bastion host by using Amazon EC2 in a private subnet within the VPC with a NAT gateway.
Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.

Answer: B

Explanation:
In this scenario, the company requires that access to the Amazon SageMaker notebook remain within the AWS internal network, avoiding the public internet. By creating a VPC interface endpoint for SageMaker, the company can ensure that traffic to the SageMaker notebook remains internal to the VPC and is accessible over a private connection. The VPC interface endpoint allows private network access to AWS services, and it operates over AWS's internal network, respecting the security and connectivity policies the company requires.
This solution requires minimal development effort compared to options involving bastion hosts or NAT gateways, as it directly provides private network access to the SageMaker notebook.

NEW QUESTION # 107
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. Confusion matrix
B. Area under the curve
C. Root Mean Square Error (RMSE)
D. Residual plots

Answer: D

NEW QUESTION # 108
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

A. Create VPC peering with Amazon VPC hosting Amazon SageMaker.
B. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
C. Create a NAT gateway within the corporate VPC.
D. Route Amazon SageMaker traffic through an on-premises network.

Answer: B

Explanation:
Explanation
To enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances, the company should create Amazon SageMaker VPC interface endpoints within the corporate VPC. A VPC interface endpoint is a gateway that enables private connections between the VPC and supported AWS services without requiring an internet gateway, a NAT device, a VPN connection, or an AWS Direct Connect connection. The instances in the VPC do not need to connect to the public internet in order to communicate with the Amazon SageMaker service. The VPC interface endpoint connects the VPC directly to the Amazon SageMaker service using AWS PrivateLink, which ensures that the traffic between the VPC and the service does not leave the AWS network1.
References:
1: Connect to SageMaker Within your VPC - Amazon SageMaker

NEW QUESTION # 109
A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker.
Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?

A. Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3.
B. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster.
C. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.
D. Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3.

Answer: C

Explanation:
Explanation
The solution that will accomplish the necessary transformation to train the Amazon SageMaker model with the least amount of administrative overhead is to crawl the data using AWS Glue crawlers, write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3. This solution leverages the serverless capabilities of AWS Glue to automatically discover the schema of the data sources, and to perform the data integration and transformation without requiring any cluster management or configuration. The output in CSV format is compatible with Amazon SageMaker and can be easily loaded into a training job. References: AWS Glue, Amazon SageMaker

NEW QUESTION # 110
A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.
How can the data scientist meet these requirements?

A. Call the CreateNotebookInstanceLifecycleConfig API operation
B. Call the UpdateNotebookInstanceLifecycleConfig API operation
C. Stop and then restart the SageMaker notebook instance
D. Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance

Answer: C

Explanation:
The correct solution for updating the software on a SageMaker notebook instance is to stop and then restart the notebook instance. This will automatically apply the latest security and software updates provided by SageMaker1 The other options are incorrect because they either do not update the software or require unnecessary steps.
For example:
* Option A calls the CreateNotebookInstanceLifecycleConfig API operation. This operation creates a lifecycle configuration, which is a set of shell scripts that run when a notebook instance is created or started. A lifecycle configuration can be used to customize the notebook instance, such as installing additional libraries or packages. However, it does not update the software on the notebook instance2
* Option B creates a new SageMaker notebook instance and mounts the Amazon Elastic Block Store (Amazon EBS) volume from the original instance. This option will create a new notebook instance with the latest software, but it will also incur additional costs and require manual steps to transfer the data and settings from the original instance3
* Option D calls the UpdateNotebookInstanceLifecycleConfig API operation. This operation updates an existing lifecycle configuration. As explained in option A, a lifecycle configuration does not update the software on the notebook instance4
1: Amazon SageMaker Notebook Instances - Amazon SageMaker
2: CreateNotebookInstanceLifecycleConfig - Amazon SageMaker
3: Create a Notebook Instance - Amazon SageMaker
4: UpdateNotebookInstanceLifecycleConfig - Amazon SageMaker

NEW QUESTION # 111
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