VALID DUMPS MLS-C01 PPT, MLS-C01 VALID EXAM BLUEPRINT

Valid Dumps MLS-C01 Ppt, MLS-C01 Valid Exam Blueprint

Valid Dumps MLS-C01 Ppt, MLS-C01 Valid Exam Blueprint

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The AWS Certified Machine Learning - Specialty exam is a certification program offered by Amazon Web Services (AWS) that validates the skills of professionals in the field of machine learning. AWS Certified Machine Learning - Specialty certification is designed for individuals who have a strong understanding of the foundations of machine learning and are proficient in building and deploying machine learning solutions on AWS. MLS-C01 Exam covers a wide range of topics, including data engineering, data pre-processing, feature engineering, model selection and training, and deployment and monitoring of machine learning models.

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The Lead2Passed MLS-C01 exam questions are being offered in three different formats. These formats are MLS-C01 PDF dumps files, desktop practice test software, and web-based practice test software. All these three MLS-C01 exam dumps formats contain the Real MLS-C01 Exam Questions that assist you in your AWS Certified Machine Learning - Specialty practice exam preparation and finally, you will be confident to pass the final AWS Certified Machine Learning - Specialty (MLS-C01) exam easily.

To earn the AWS Certified Machine Learning - Specialty certification, candidates must pass the MLS-C01 exam, which consists of 65 multiple-choice and multiple-response questions. MLS-C01 exam covers a broad range of topics, including data engineering, feature engineering, model selection and training, and deployment and monitoring of ML models. MLS-C01 Exam also tests the candidate's knowledge of AWS's various ML services, such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q49-Q54):

NEW QUESTION # 49
A car company is developing a machine learning solution to detect whether a car is present in an image. The image dataset consists of one million images. Each image in the dataset is 200 pixels in height by 200 pixels in width. Each image is labeled as either having a car or not having a car.
Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?

  • A. Use a deep convolutional neural network (CNN) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.
  • B. Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.
  • C. Use a deep convolutional neural network (CNN) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.
  • D. Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.

Answer: C

Explanation:
A deep convolutional neural network (CNN) classifier is a suitable architecture for image classification tasks, as it can learn features from the images and reduce the dimensionality of the input. A linear output layer that outputs the probability that an image contains a car is appropriate for a binary classification problem, as it can produce a single scalar value between 0 and 1. A softmax output layer is more suitable for a multi-class classification problem, as it can produce a vector of probabilities that sum up to 1. A deep multilayer perceptron (MLP) classifier is not as effective as a CNN for image classification, as it does not exploit the spatial structure of the images and requires a large number of parameters to process the high-dimensional input. References:
AWS Certified Machine Learning - Specialty Exam Guide
AWS Training - Machine Learning on AWS
AWS Whitepaper - An Overview of Machine Learning on AWS


NEW QUESTION # 50
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?

  • A. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
  • B. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
  • C. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
  • D. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.

Answer: A

Explanation:
Explanation
Amazon SageMaker notebook instances are fully managed environments that provide an integrated Jupyter notebook interface for data exploration, analysis, and machine learning. Amazon SageMaker notebook instances are based on EC2 instances that run within AWS service accounts, not within customer accounts.
This means that the ML Specialist cannot find the Amazon SageMaker notebook instance's EC2 instance or EBS volume within the VPC, as they are not visible or accessible to the customer. However, the ML Specialist can still take a snapshot of the EBS volume by using the Amazon SageMaker console or API. The ML Specialist can also use VPC interface endpoints to securely connect the Amazon SageMaker notebook instance to the resources within the VPC, such as Amazon S3 buckets, Amazon EFS file systems, or Amazon RDS databases


NEW QUESTION # 51
A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS). The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.
How should the data scientist meet these requirements?

  • A. Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.
  • B. Run a SageMaker training job with an EFS file system as the data source.
  • C. Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.
  • D. Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.

Answer: C


NEW QUESTION # 52
An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.
What combination of services is the MOST efficient to accomplish the task?

  • A. Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
  • B. Amazon Transcribe, Amazon Translate, and Amazon Comprehend
  • C. Amazon Transcribe, Amazon Translate and Amazon SageMaker BlazingText
  • D. Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq

Answer: B

Explanation:
https://aws.amazon.com/getting-started/hands-on/analyze-sentiment-comprehend/


NEW QUESTION # 53
A Machine Learning Specialist needs to move and transform data in preparation for training Some of the data needs to be processed in near-real time and other data can be moved hourly There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data Which of the following services can feed data to the MapReduce jobs? (Select TWO )

  • A. AWSDMS
  • B. Amazon ES
  • C. AWS Data Pipeline
  • D. Amazon Kinesis
  • E. Amazon Athena

Answer: C,D

Explanation:
Amazon Kinesis and AWS Data Pipeline are two services that can feed data to the Amazon EMR MapReduce jobs. Amazon Kinesis is a service that can ingest, process, and analyze streaming data in real time. Amazon Kinesis can be integrated with Amazon EMR to run MapReduce jobs on streaming data sources, such as web logs, social media, IoT devices, and clickstreams. Amazon Kinesis can handle data that needs to be processed in near-real time, such as for anomaly detection, fraud detection, or dashboarding. AWS Data Pipeline is a service that can orchestrate and automate data movement and transformation across various AWS services and on-premises data sources. AWS Data Pipeline can be integrated with Amazon EMR to run MapReduce jobs on batch data sources, such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon Redshift. AWS Data Pipeline can handle data that can be moved hourly, such as for data warehousing, reporting, or machine learning.
AWSDMS is not a valid service name. AWS Database Migration Service (AWS DMS) is a service that can migrate data from various sources to various targets, but it does not support streaming data or MapReduce jobs.
Amazon Athena is a service that can query data stored in Amazon S3 using standard SQL, but it does not feed data to Amazon EMR or run MapReduce jobs.
Amazon ES is a service that provides a fully managed Elasticsearch cluster, which can be used for search, analytics, and visualization, but it does not feed data to Amazon EMR or run MapReduce jobs. References:
Using Amazon Kinesis with Amazon EMR - Amazon EMR
AWS Data Pipeline - Amazon Web Services
Using AWS Data Pipeline to Run Amazon EMR Jobs - AWS Data Pipeline


NEW QUESTION # 54
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