AMAZON MLS-C01 RELIABLE EXAM VCE | FRENQUENT MLS-C01 UPDATE

Amazon MLS-C01 Reliable Exam Vce | Frenquent MLS-C01 Update

Amazon MLS-C01 Reliable Exam Vce | Frenquent MLS-C01 Update

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Amazon MLS-C01 certification exams play a significant role to verify skills, experience, and knowledge in a specific technology. Enrollment in the AWS Certified Machine Learning - Specialty MLS-C01 is open to everyone. Participants in the AWS Certified Machine Learning - Specialty MLS-C01 come from all over the world and receive the credentials for the Amazon MLS-C01. They can quickly advance their careers in the fiercely competitive market and benefit from certification after earning the AWS Certified Machine Learning - Specialty MLS-C01 badge.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q207-Q212):

NEW QUESTION # 207
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. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker.
  • B. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
  • C. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.
  • D. 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.

Answer: D


NEW QUESTION # 208
Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

  • A. The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ' 0.32
  • B. The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 -
    0.32).
  • C. The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is
    1312%
  • D. The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20
    85%

Answer: C

Explanation:
Explanation
The true class frequency for Romance is the percentage of movies that are actually Romance out of all the movies. This can be calculated by dividing the sum of the true values for Romance by the total number of movies. The predicted class frequency for Adventure is the percentage of movies that are predicted to be Adventure out of all the movies. This can be calculated by dividing the sum of the predicted values for Adventure by the total number of movies. Based on the confusion matrix, the true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 13.12%. References: Confusion Matrix, Classification Metrics


NEW QUESTION # 209
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 AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics
  • B. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics
  • C. Use Amazon Translate with 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: D

Explanation:
Explanation
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. It can analyze text documents and identify the key topics, entities, sentiments, languages, and more. In this case, Amazon Comprehend can be used with the transcribed files from Amazon Transcribe to extract the main topics that are being discussed by the call operators. This can help to understand the common issues and concerns of the customers, and provide insights for further analysis and improvement. References:
Amazon Comprehend - Amazon Web Services
AWS Certified Machine Learning - Specialty Sample Questions


NEW QUESTION # 210
A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term. The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion.
How will the data scientist MOST effectively model the problem?

  • A. Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using historical data through a supervised learning approach.
  • B. The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi- agent reinforcement learning problem.
  • C. The data scientist should obtain the optimal equilibrium policy by formulating this problem as a single- agent reinforcement learning problem.
  • D. Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using unlabeled simulated data representing the new traffic patterns in the city and applying an unsupervised learning approach.

Answer: B

Explanation:
The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem. This is because:
* Multi-agent reinforcement learning (MARL) is a subfield of reinforcement learning that deals with learning and coordination of multiple agents that interact with each other and the environment 1. MARL can be applied to problems that involve distributed decision making, such as traffic signal control, where each traffic light can be modeled as an agent that observes the traffic state and chooses an action (e.g., changing the signal phase) to optimize a reward function (e.g., minimizing the delay or congestion) 2.
* A correlated equilibrium is a solution concept in game theory that generalizes the notion of Nash equilibrium. It is a probability distribution over the joint actions of the agents that satisfies the following condition: no agent can improve its expected payoff by deviating from the distribution, given that it knows the distribution and the actions of the other agents 3. A correlated equilibrium can capture the correlation among the agents' actions, which is useful for modeling the traffic behavior at each light that is subject to a small stochastic error term.
* A correlated equilibrium policy is a policy that induces a correlated equilibrium in a MARL setting. It can be obtained by using various methods, such as policy gradient, actor-critic, or Q-learning algorithms, that can learn from the feedback of the environment and the communication among the agents 4. A correlated equilibrium policy can achieve a better performance than a Nash equilibrium policy, which assumes that the agents act independently and ignore the correlation among their actions 5.
Therefore, by obtaining a correlated equilibrium policy by formulating this problem as a MARL problem, the data scientist can most effectively model the traffic behavior and reduce congestion.
Multi-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning for Traffic Signal Control: A Survey Correlated Equilibrium Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments Correlated Q-Learning


NEW QUESTION # 211
A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance How should the records be stored in Amazon S3 to improve query performance?

  • A. RecordIO
  • B. CSV files
  • C. Parquet files
  • D. Compressed JSON

Answer: C


NEW QUESTION # 212
......

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