Zoraintech

James Ross James Ross

0 Course Enrolled • 0 Course Completed

Biography

Reliable MLS-C01 Practice Materials - MLS-C01 Real Study Guide - TestPassed

What's more, part of that TestPassed MLS-C01 dumps now are free: https://drive.google.com/open?id=1EtaLwyEWwpu467vhYhZeeYmO12Y1S3ll

The TestPassed is one of the best platforms that has been helping Amazon MLS-C01 certification exam candidates for many years. Over this long time period, the AWS Certified Machine Learning - Specialty MLS-C01 exam questions helped many AWS Certified Machine Learning - Specialty MLS-C01 exam candidates to pass their certification exam. Now the AWS Certified Machine Learning - Specialty MLS-C01 Exam Questions have become the first choice for instant and complete MLS-C01 exam preparation. As far as the standard of MLS-C01 real questions is concerned, the AWS Certified Machine Learning - Specialty MLS-C01 actual questions are designed and verified by qualified Amazon MLS-C01 exam trainers.

Amazon MLS-C01 certification exam is a challenging exam that requires a comprehensive understanding of machine learning concepts and best practices. MLS-C01 exam covers a wide range of topics, including supervised and unsupervised learning, deep learning, reinforcement learning, natural language processing, and computer vision. Candidates are also expected to have a solid understanding of AWS services and tools that are used for building and deploying machine learning models, such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

The MLS-C01 Certification is highly valued in the industry, as it demonstrates an individual's expertise in machine learning and their ability to work with AWS services. It is also a great way for individuals to differentiate themselves from their peers and advance their careers. AWS Certified Machine Learning - Specialty certification can help individuals secure roles such as data scientist, machine learning engineer, and AI developer.

>> MLS-C01 Test Sample Questions <<

Precise MLS-C01 Test Sample Questions Offers you high-effective Actual Amazon AWS Certified Machine Learning - Specialty Exam Products

You can try the Amazon MLS-C01 exam dumps demo before purchasing. If you like our AWS Certified Machine Learning - Specialty (MLS-C01) exam questions features, you can get the full version after payment. TestPassed AWS Certified Machine Learning - Specialty (MLS-C01) dumps give surety to confidently pass the AWS Certified Machine Learning - Specialty (MLS-C01) exam on the first attempt.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q217-Q222):

NEW QUESTION # 217
A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.
Which storage option provides the most processing flexibility and will allow access control with IAM?

A. Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.
B. Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
C. Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
D. Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.

Answer: D

Explanation:
The best storage option for the trucking company is to use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies. A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Amazon S3 is the ideal choice for building a data lake because it offers high durability, scalability, availability, and security. You can store any type of data in Amazon S3, such as images, videos, audio, text, etc. You can also use AWS services such as Amazon Rekognition, Amazon SageMaker, and Amazon EMR to analyze and process the data in the data lake. To ensure the data is only accessible to specific IAM users, you can use bucket policies to grant or deny access to the S3 buckets based on the IAM user's identity or role. Bucket policies are JSON documents that specify the permissions for the bucket and the objects in it. You can use conditions to restrict access based on various factors, such as IP address, time, source, etc. By using bucket policies, you can control who can access the data in the data lake and what actions they can perform on it.
References:
* AWS Machine Learning Specialty Exam Guide
* AWS Machine Learning Training - Build a Data Lake Foundation with Amazon S3
* AWS Machine Learning Training - Using Bucket Policies and User Policies

NEW QUESTION # 218
A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

A. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
B. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
C. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
D. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

Answer: D

Explanation:
Explanation
AWS KMS is a service that provides encryption and key management for data stored in AWS services and applications. AWS KMS can generate and manage encryption keys that are used to encrypt and decrypt data at rest and in transit. AWS KMS can also integrate with other AWS services, such as Amazon S3 and Amazon SageMaker, to enable encryption of data using the keys stored in AWS KMS. Amazon S3 is a service that provides object storage for data in the cloud. Amazon S3 can use AWS KMS to encrypt data at rest using server-side encryption with AWS KMS-managed keys (SSE-KMS). Amazon SageMaker is a service that provides a platform for building, training, and deploying machine learning models. Amazon SageMaker can use AWS KMS to encrypt data at rest on the SageMaker instances and volumes, as well as data in transit between SageMaker and other AWS services. AWS Glue is a service that provides a serverless data integration platform for data preparation and transformation. AWS Glue can use AWS KMS to encrypt data at rest on the Glue Data Catalog and Glue ETL jobs. AWS Glue can also use built-in or custom classifiers to identify and redact sensitive data, such as credit card numbers, from the customer data1234 The other options are not valid or secure ways to encrypt the data and protect the credit card information.
Using a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC is not a good practice, as custom encryption algorithms are not recommended for security and may have flaws or vulnerabilities. Using the SageMaker DeepAR algorithm to randomize the credit card numbers is not a good practice, as DeepAR is a forecasting algorithm that is not designed for data anonymization or encryption. Using an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers is not a good practice, as IAM policies are not meant for data encryption, but for access control and authorization. Amazon Kinesis is a service that provides real-time data streaming and processing, but it does not have the capability to automatically discard or insert data values. Using an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC is not a good practice, as launch configurations are not meant for data encryption, but for specifying the instance type, security group, and user data for the SageMaker instance. Using the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers is not a good practice, as PCA is a dimensionality reduction algorithm that is not designed for data anonymization or encryption.

NEW QUESTION # 219
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 apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
B. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
C. 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.
D. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.

Answer: B

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 # 220
A data engineer is preparing a dataset that a retail company will use to predict the number of visitors to stores.
The data engineer created an Amazon S3 bucket. The engineer subscribed the S3 bucket to an AWS Data Exchange data product for general economic indicators. The data engineer wants to join the economic indicator data to an existing table in Amazon Athena to merge with the business data. All these transformations must finish running in 30-60 minutes.
Which solution will meet these requirements MOST cost-effectively?

A. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda Function Program the Lambda function to run an AWS Glue job that will merge the existing business data with the Athena table Write the results back to Amazon S3.
B. Configure the AWS Data Exchange product as a producer for an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery stream to transfer the data to Amazon S3 Run an AWS Glue job that will merge the existing business data with the Athena table. Write the result set back to Amazon S3.
C. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to use Amazon SageMaker Data Wrangler to merge the existing business data with the Athena table. Write the result set back to Amazon S3.
D. Provision an Amazon Redshift cluster. Subscribe to the AWS Data Exchange product and use the product to create an Amazon Redshift Table Merge the data in Amazon Redshift. Write the results back to Amazon S3.

Answer: C

Explanation:
The most cost-effective solution is to use an S3 event to trigger a Lambda function that uses SageMaker Data Wrangler to merge the data. This solution avoids the need to provision and manage any additional resources, such as Kinesis streams, Firehose delivery streams, Glue jobs, or Redshift clusters. SageMaker Data Wrangler provides a visual interface to import, prepare, transform, and analyze data from various sources, including AWS Data Exchange products. It can also export the data preparation workflow to a Python script that can be executed by a Lambda function. This solution can meet the time requirement of 30-60 minutes, depending on the size and complexity of the data.
References:
* Using Amazon S3 Event Notifications
* Prepare ML Data with Amazon SageMaker Data Wrangler
* AWS Lambda Function

NEW QUESTION # 221
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 Which approach will provide the information required for further analysis?

A. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics
B. Use Amazon Translate with the transcribed files to train and build a model for the key topics
C. Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics
D. Use Amazon Comprehend with the transcribed files to build the key topics

Answer: B

NEW QUESTION # 222
......

With the development of computer hi-tech, the computer application is widely used in recent years. The demand of the higher position about computer is increasing. MLS-C01 exam vce files help people who are interested in Amazon company. If you have a useful certification, you will have outstanding advantage over other applicants while interviewing. Our MLS-C01 Exam Vce files help you go through examination and get certifications.

Reliable MLS-C01 Exam Questions: https://www.testpassed.com/MLS-C01-still-valid-exam.html

P.S. Free & New MLS-C01 dumps are available on Google Drive shared by TestPassed: https://drive.google.com/open?id=1EtaLwyEWwpu467vhYhZeeYmO12Y1S3ll

James Ross James Ross

Biography

Shopping cart