Open the auxiliary access

Firefly Open Source Community

   Login   |   Register   |
New_Topic
Firefly Open Source Community › Development Kit › Baidu Face Recognition Kit› Premium MLS-C01 Files & Valid MLS-C01 Study Materi ...
New Topic
Print Previous Topic Next Topic

[General] Premium MLS-C01 Files & Valid MLS-C01 Study Materials

keithbe431

129

Credits

0

Prestige

0

Contribution

registered members

Rank: 2

Credits
129

【General】 Premium MLS-C01 Files & Valid MLS-C01 Study Materials

Posted at yesterday 20:23      View：5 | Replies：0        Print      Only Author   [Copy Link] 1#
What's more, part of that DumpsReview MLS-C01 dumps now are free: https://drive.google.com/open?id=1tFDHuaIwWGvvtbe_adMuJ2ee9yzthl00
Our MLS-C01 study materials are very popular in the international market and enjoy wide praise by the people in and outside the circle. We have shaped our MLS-C01 exam questions into a famous and top-ranking brand and we enjoy well-deserved reputation among the clients. Our MLS-C01 learning guide boosts many outstanding and superior advantages which other same kinds of exam materials don’t have. And we are very reliable in every aspect no matter on the quality or the according service.
To prepare for the AWS Certified Machine Learning - Specialty certification exam, candidates can take advantage of various resources provided by AWS, including online courses, practice exams, and whitepapers. Candidates can also attend training sessions and workshops offered by AWS partners and take advantage of AWS support and consulting services.
Amazon - MLS-C01 Accurate Premium FilesDumpsReview’s exam dumps guarantee your success with a promise of returning back the amount you paid. Such an in itself is the best proof of the unique quality of our product and its ultimate utility for you. Try MLS-C01 Dumps and ace your upcoming MLS-C01 certification test, securing the best percentage of your academic career. If you didn't pass MLS-C01 exam, we guarantee you will get full refund.
Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q119-Q124):NEW QUESTION # 119
A data scientist is designing a repository that will contain many images of vehicles. The repository must scale automatically in size to store new images every day. The repository must support versioning of the images.
The data scientist must implement a solution that maintains multiple immediately accessible copies of the data in different AWS Regions.
Which solution will meet these requirements?
  • A. Amazon Elastic Block Store (Amazon EBS) with snapshots that are shared in a secondary Region
  • B. Amazon S3 with S3 Cross-Region Replication (CRR)
  • C. Amazon Elastic File System (Amazon EFS) Standard storage that is configured with Regional availability
  • D. AWS Storage Gateway Volume Gateway
Answer: B
Explanation:
For a repository containing a large and dynamically scaling collection of images, Amazon S3 is ideal due to its scalability and versioning capabilities. Amazon S3 natively supports automatic scaling to accommodate increasing storage needs and allows versioning, which enables tracking and managing different versions of objects.
To meet the requirement of maintaining multiple, immediately accessible copies of data across AWS Regions, S3 Cross-Region Replication (CRR) can be enabled. CRR automatically replicates new or updated objects to a specified destination bucket in another AWS Region, ensuring low-latency access and disaster recovery.
By setting up CRR with versioning enabled, the data scientist can achieve a multi-Region, scalable, and version-controlled repository in Amazon S3.

NEW QUESTION # 120
For the given confusion matrix, what is the recall and precision of the model?

  • A. Recall = 0.8 Precision = 0.92
  • B. Recall = 0.84 Precision = 0.8
  • C. Recall = 0.92 Precision = 0.8
  • D. Recall = 0.92 Precision = 0.84
Answer: C
Explanation:
Recall and precision are two metrics that can be used to evaluate the performance of a classification model.
Recall is the ratio of true positives to the total number of actual positives, which measures how well the model can identify all the relevant cases. Precision is the ratio of true positives to the total number of predicted positives, which measures how accurate the model is when it makes a positive prediction. Based on the confusion matrix in the image, we can calculate the recall and precision as follows:
* Recall = TP / (TP + FN) = 12 / (12 + 1) = 0.92
* Precision = TP / (TP + FP) = 12 / (12 + 3) = 0.8
Where TP is the number of true positives, FN is the number of false negatives, and FP is the number of false positives. Therefore, the recall and precision of the model are 0.92 and 0.8, respectively.

NEW QUESTION # 121
A Machine Learning Specialist is building a supervised model that will evaluate customers' satisfaction with their mobile phone service based on recent usage The model's output should infer whether or not a customer is likely to switch to a competitor in the next 30 days Which of the following modeling techniques should the Specialist use1?
  • A. Time-series prediction
  • B. Binary classification
  • C. Regression
  • D. Anomaly detection
Answer: C

NEW QUESTION # 122
A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model What should the Specialist do to address the performance issues with the current solution?
  • A. Use the SageMaker batch transform feature
  • B. Compress the training data into Apache Parquet format.
  • C. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.
  • D. Ensure that the input mode for the training job is set to Pipe.
Answer: D
Explanation:
The input mode for the training job determines how the training data is transferred from Amazon S3 to the SageMaker instance. There are two input modes: File and Pipe. File mode copies the entire training dataset from S3 to the local file system of the instance before starting the training job. This can cause a long delay before the training job launches, especially if the dataset is large. Pipe mode streams the data from S3 to the instance as the training job runs. This can reduce the startup time and improve the I/O throughput, as the data is read in smaller batches. Therefore, to address the performance issues with the current solution, the Specialist should ensure that the input mode for the training job is set to Pipe. This can be done by using the SageMaker Python SDK and setting the input_mode parameter to Pipe when creating the estimator or the fit method12. Alternatively, this can be done by using the AWS CLI and setting the InputMode parameter to Pipe when creating the training job3.
References:
* Access Training Data - Amazon SageMaker
* Choosing Data Input Mode Using the SageMaker Python SDK - Amazon SageMaker
* CreateTrainingJob - Amazon SageMaker Service

NEW QUESTION # 123
A company that runs an online library is implementing a chatbot using Amazon Lex to provide book recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are only three categories implemented as the custom slot types: "comedy," "adventure," and "documentary." A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because Amazon Lex cannot understand the category spoken by users with utterances such as "funny," "fun," and "humor." The ML specialist needs to fix the problem without changing the Lambda code or data in DynamoDB.
How should the ML specialist fix the problem?
  • A. Add the unrecognized words in the enumeration values list as new values in the slot type.
  • B. Create a new custom slot type, add the unrecognized words to this slot type as enumeration values, and use this slot type for the slot.
  • C. Use the AMAZON.SearchQuery built-in slot types for custom searches in the database.
  • D. Add the unrecognized words as synonyms in the custom slot type.
Answer: D
Explanation:
The best way to fix the problem without changing the Lambda code or data in DynamoDB is to add the unrecognized words as synonyms in the custom slot type. This way, Amazon Lex can resolve the synonyms to the corresponding slot values and pass them to the Lambda function. For example, if the slot type has a value "comedy" with synonyms "funny", "fun", and "humor", then any of these words entered by the user will be resolved to "comedy" and the Lambda function can query the DynamoDB table for the book titles in that category. Adding synonyms to the custom slot type can be done easily using the Amazon Lex console or API, and does not require any code changes.
The other options are not correct because:
Option A: Adding the unrecognized words in the enumeration values list as new values in the slot type would not fix the problem, because the Lambda function and the DynamoDB table are not aware of these new values. The Lambda function would not be able to query the DynamoDB table for the book titles in the new categories, and the request would still fail. Moreover, adding new values to the slot type would increase the complexity and maintenance of the chatbot, as the Lambda function and the DynamoDB table would have to be updated accordingly.
Option B: Creating a new custom slot type, adding the unrecognized words to this slot type as enumeration values, and using this slot type for the slot would also not fix the problem, for the same reasons as option A. The Lambda function and the DynamoDB table would not be able to handle the new slot type and its values, and the request would still fail. Furthermore, creating a new slot type would require more effort and time than adding synonyms to the existing slot type.
Option C: Using the AMAZON.SearchQuery built-in slot types for custom searches in the database is not a suitable approach for this use case. The AMAZON.SearchQuery slot type is used to capture free-form user input that corresponds to a search query. However, this slot type does not perform any validation or resolution of the user input, and passes the raw input to the Lambda function. This means that the Lambda function would have to handle the logic of parsing and matching the user input to the DynamoDB table, which would require changing the Lambda code and adding more complexity to the solution.
References:
Custom slot type - Amazon Lex
Using Synonyms - Amazon Lex
Built-in Slot Types - Amazon Lex

NEW QUESTION # 124
......
Based on your situation, including the available time, your current level of knowledge, our MLS-C01 study materials will develop appropriate plans and learning materials. You can use MLS-C01 test questions when you are available, to ensure the efficiency of each use, this will have a very good effect. You don't have to worry about yourself or anything else. Our MLS-C01 Study Materials allow you to learn at any time. And with our MLS-C01 learning guide, you can pass the MLS-C01 exam with the least time and effort.
Valid MLS-C01 Study Materials: https://www.dumpsreview.com/MLS-C01-exam-dumps-review.html
What's more, part of that DumpsReview MLS-C01 dumps now are free: https://drive.google.com/open?id=1tFDHuaIwWGvvtbe_adMuJ2ee9yzthl00
https://www.easy4engine.com
Reply

Use props Report

Return to List
High mode
B Color Image Link Quote Code Smilies
You need to log in before you can reply Login | Register

This forum Credits Rules

  •  

Service Hotline : (+86)18688117175
Contact us
E-mail : global@t-firefly.com
Copyright © 2014-2022, FIREFLY TECHNOLOGY CO.,LTD. All Rightts Reserved. 粤ICP备14022046号-2
Quick Reply Back to top Back to list