Amazon MLS-C01 Valid Test Tips - MLS-C01 Exam Questions Fee

These AWS Certified Machine Learning - Specialty (MLS-C01) practice test covers all the topics of the MLS-C01 test and includes real MLS-C01 questions. If you are attempting the MLS-C01 examination for the first time, you will get an exact idea about the MLS-C01 exam and how you can clear it with flying colors. These Amazon MLS-C01 Questions are available in desktop MLS-C01 practice exam software, web-based MLS-C01 practice test, and AWS Certified Machine Learning - Specialty (MLS-C01) dumps pdf format.

The AWS Certified Machine Learning - Specialty exam consists of 65 multiple-choice and multiple-response questions, and candidates have three hours to complete the exam. MLS-C01 exam tests the candidate's ability to design, implement, deploy, and maintain machine learning solutions on the AWS platform. To be eligible for the exam, candidates need to have a minimum of one year of experience in developing and maintaining machine learning solutions on the AWS platform. Upon successful completion of the exam, candidates will receive the AWS Certified Machine Learning - Specialty certification, which is recognized globally and demonstrates their expertise in the field of machine learning on the AWS platform.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is designed for individuals who want to validate their expertise in machine learning (ML) on the Amazon Web Services (AWS) platform. AWS Certified Machine Learning - Specialty certification is ideal for data scientists, machine learning developers, and individuals who want to demonstrate their skills in building, training, deploying, and managing ML models on AWS.

>> Amazon MLS-C01 Valid Test Tips <<

MLS-C01 Exam Questions Fee | Valid MLS-C01 Exam Cram

In order to gain more competitive advantage in the interview, more and more people have been eager to obtain the MLS-C01 certification. They believe that passing certification is a manifestation of their ability, and they have been convinced that obtaining a MLS-C01 certification can help them find a better job. Our MLS-C01 test guides have a higher standard of practice and are rich in content. If you are anxious about how to get MLS-C01 Certification, considering purchasing our MLS-C01 study tool is a wise choice and you will not feel regretted. Our learning materials will successfully promote your acquisition of certification. Our MLS-C01 qualification test closely follow changes in the exam outline and practice.

Understanding functional and technical aspects of AWS Certified Machine Learning - Specialty Data Engineering

The following will be discussed in AMAZON MLS-C01 Exam Dumps:

Identify and implement a data-ingestion solution
Create data repositories for machine learning
Identify and implement a data-transformation solution

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q192-Q197):

NEW QUESTION # 192
A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.
How should the Data Science team configure the notebook instance placement to meet these requirements?

A. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.
B. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
C. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
D. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker.

Answer: B

Explanation:
We must use the VPC endpoint (either Gateway Endpoint or Interface Endpoint)to comply with this requirement "Data communication traffic must stay within the AWS network".
https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-interface-endpoint.html

NEW QUESTION # 193
A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable recall metric. The Data Scientist has already tried varying the number and size of the MLP's hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible.
Which techniques should be used to meet these requirements?

A. Add class weights to the MLP's loss function and then retrain
B. Train an anomaly detection model instead of an MLP
C. Train an XGBoost model instead of an MLP
D. Gather more data using Amazon Mechanical Turk and then retrain

Answer: C

NEW QUESTION # 194
A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences, and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?

A. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database.
B. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database.
C. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database.
D. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.

Answer: A

NEW QUESTION # 195
A Machine Learning Specialist deployed a model that provides product recommendations on a company's website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less.
The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago.
Which method should the Specialist try to improve model performance?

A. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
B. The model should be periodically retrained using the original training data plus new data as product inventory changes.
C. The model's hyperparameters should be periodically updated to prevent drift.
D. The model needs to be completely re-engineered because it is unable to handle product inventory changes.

Answer: B

NEW QUESTION # 196
A media company is building a computer vision model to analyze images that are on social media. The model consists of CNNs that the company trained by using images that the company stores in Amazon S3. The company used an Amazon SageMaker training job in File mode with a single Amazon EC2 On-Demand Instance.
Every day, the company updates the model by using about 10,000 images that the company has collected in the last 24 hours. The company configures training with only one epoch. The company wants to speed up training and lower costs without the need to make any code changes.
Which solution will meet these requirements?

A. Instead Of On-Demand Instances, configure the SageMaker training job to use Spot Instances. Make no Other changes.
B. Instead of File mode, configure the SageMaker training job to use Pipe mode. Ingest the data from a pipe.
C. Instead Of On-Demand Instances, configure the SageMaker training job to use Spot Instances.Implement model checkpoints.
D. Instead Of File mode, configure the SageMaker training job to use FastFile mode with no Other changes.

Answer: A

Explanation:
Explanation
The solution C will meet the requirements because it uses Amazon SageMaker Spot Instances, which are unused EC2 instances that are available at up to 90% discount compared to On-Demand prices. Amazon SageMaker Spot Instances can speed up training and lower costs by taking advantage of the spare EC2 capacity. The company does not need to make any code changes to use Spot Instances, as it can simply enable the managed spot training option in the SageMaker training job configuration. The company also does not need to implement model checkpoints, as it is using only one epoch for training, which means the model will not resume from a previous state1.
The other options are not suitable because:
Option A: Configuring the SageMaker training job to use Pipe mode instead of File mode will not speed up training or lower costs significantly. Pipe mode is a data ingestion mode that streams data directly from S3 to the training algorithm, without copying the data to the local storage of the training instance.
Pipe mode can reduce the startup time of the training job and the disk space usage, but it does not affect the computation time or the instance price. Moreover, Pipe mode may require some code changes to handle the streaming data, depending on the training algorithm2.
Option B: Configuring the SageMaker training job to use FastFile mode instead of File mode will not speed up training or lower costs significantly. FastFile mode is a data ingestion mode that copies data from S3 to the local storage of the training instance in parallel with the training process. FastFile mode can reduce the startup time of the training job and the disk space usage, but it does not affect the computation time or the instance price. Moreover, FastFile mode is only available for distributed training jobs that use multiple instances, which is not the case for the company3.
Option D: Configuring the SageMaker training job to use Spot Instances and implementing model checkpoints will not meet the requirements without the need to make any code changes. Model checkpoints are a feature that allows the training job to save the model state periodically to S3, and resume from the latest checkpoint if the training job is interrupted. Model checkpoints can help to avoid losing the training progress and ensure the model convergence, but they require some code changes to implement the checkpointing logic and the resuming logic4.
References:
1: Managed Spot Training - Amazon SageMaker
2: Pipe Mode - Amazon SageMaker
3: FastFile Mode - Amazon SageMaker
4: Checkpoints - Amazon SageMaker

NEW QUESTION # 197
......

MLS-C01 Exam Questions Fee: https://www.dumpstorrent.com/MLS-C01-exam-dumps-torrent.html

Alan Stone Alan Stone

Biography

Amazon MLS-C01 Valid Test Tips - MLS-C01 Exam Questions Fee

MLS-C01 Exam Questions Fee | Valid MLS-C01 Exam Cram

Understanding functional and technical aspects of AWS Certified Machine Learning - Specialty Data Engineering

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q192-Q197):

Archives