Open the auxiliary access

Firefly Open Source Community

   Login   |   Register   |
New_Topic
Firefly Open Source Community › Embedded Computer › EC-A3288C› Updated Exam MLS-C01 Question for Real Exam
New Topic
Print Previous Topic Next Topic

[General] Updated Exam MLS-C01 Question for Real Exam

edlong991

133

Credits

0

Prestige

0

Contribution

registered members

Rank: 2

Credits
133

【General】 Updated Exam MLS-C01 Question for Real Exam

Posted at 1 hour before      View：1 | Replies：0        Print      Only Author   [Copy Link] 1#
BTW, DOWNLOAD part of TorrentExam MLS-C01 dumps from Cloud Storage: https://drive.google.com/open?id=15d5xvNgW8BdHiYIgg5cp2al5KHPSsF5R
The clients can use the shortest time to prepare the exam and the learning only costs 20-30 hours. The questions and answers of our MLS-C01 study materials are refined and have simplified the most important information so as to let the clients use little time to learn. The clients only need to spare 1-2 hours to learn our MLS-C01 Study Materials each day or learn them in the weekends. Commonly speaking, people like the in-service staff or the students are busy and don’t have enough time to prepare the exam. Learning our MLS-C01 study materials can help them save the time and focus their attentions on their major things.
The AWS Certified Machine Learning - Specialty certification exam is designed to test the candidate's knowledge of AWS services and tools used for machine learning, as well as their ability to develop machine learning models and solutions, and their understanding of data preparation and evaluation techniques. MLS-C01 Exam covers a range of topics including data engineering, exploratory data analysis, feature engineering, modeling, optimization, and evaluation.
Amazon MLS-C01 Exam Questions 2026 in PDF FormatIf you have a dream to get the Amazon certification? Why don’t you begin to act? The first step is to pass MLS-C01 exam. Time will wait for no one. Only if you pass the MLS-C01 exam, can you get a better promotion. And if you want to pass it more efficiently, we must be the best partner for you. Because we are professional MLS-C01 Questions torrent provider, and our MLS-C01 training materials are worth trusting; because we make great efforts on our MLS-C01 learning guide, we do better and better in this field for more than ten years. Our MLS-C01 study guide is your best choice.
Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q210-Q215):NEW QUESTION # 210
A company wants to use automatic speech recognition (ASR) to transcribe messages that are less than 60 seconds long from a voicemail-style application. The company requires the correct identification of 200 unique product names, some of which have unique spellings or pronunciations.
The company has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts it can use to customize the chosen ASR model. The company needs to ensure that everyone can update their customizations multiple times each hour.
Which approach will maximize transcription accuracy during the development phase?
  • A. Create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. Analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.
  • B. Use a voice-driven Amazon Lex bot to perform the ASR customization. Create customer slots within the bot that specifically identify each of the required product names. Use the Amazon Lex synonym mechanism to provide additional variations of each product name as mis-transcriptions are identified in development.
  • C. Use the audio transcripts to create a training dataset and build an Amazon Transcribe custom language model. Analyze the transcripts and update the training dataset with a manually corrected version of transcripts where product names are not being transcribed correctly. Create an updated custom language model.
  • D. Use Amazon Transcribe to perform the ASR customization. Analyze the word confidence scores in the transcript, and automatically create or update a custom vocabulary file with any word that has a confidence score below an acceptable threshold value. Use this updated custom vocabulary file in all future transcription tasks.
Answer: A
Explanation:
The best approach to maximize transcription accuracy during the development phase is to create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. A custom vocabulary is a list of words and phrases that are likely to appear in your audio input, along with optional information about how to pronounce them. By using a custom vocabulary, you can improve the transcription accuracy of domain-specific terms, such as product names, that may not be recognized by the general vocabulary of Amazon Transcribe. You can also analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.
The other options are not as effective as option C for the following reasons:
* Option A is not suitable because Amazon Lex is a service for building conversational interfaces, not for transcribing voicemail messages. Amazon Lex also has a limit of 100 slots per bot, which is not enough to accommodate the 200 unique product names required by the company.
* Option B is not optimal because it relies on the word confidence scores in the transcript, which may not be accurate enough to identify all the mis-transcribed product names. Moreover, automatically creating or updating a custom vocabulary file may introduce errors or inconsistencies in the pronunciation or display of the words.
* Option D is not feasible because it requires a large amount of training data to build a custom language model. The company only has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts, which is not enough to train a robust and reliable custom language model. Additionally, creating and updating a custom language model is a time-consuming and resource-intensive process, which may not be suitable for the development phase where frequent changes are expected.
Amazon Transcribe - Custom Vocabulary
Amazon Transcribe - Custom Language Models
[Amazon Lex - Limits]

NEW QUESTION # 211
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?
  • A. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
  • B. Route Amazon SageMaker traffic through an on-premises network.
  • C. Create VPC peering with Amazon VPC hosting Amazon SageMaker.
  • D. Create a NAT gafjway within the corporate VPC.
Answer: A

NEW QUESTION # 212
A company needs to develop a model that uses a machine learning (ML) model for risk analysis. An ML engineer needs to evaluate the contribution each feature of a training dataset makes to the prediction of the target variable before the ML engineer selects features.
How should the ML engineer predict the contribution of each feature?
  • A. Use the Amazon SageMaker Data Wrangler multicollinearity measurement features and the principal component analysis (PCA) algorithm to calculate the variance of the dataset along multiple directions in the feature space.
  • B. Use an Amazon SageMaker Data Wrangler quick model visualization to find feature importance scores that are between 0.5 and 1.
  • C. Use the Amazon SageMaker Data Wrangler bias report to identify potential biases in the data related to feature engineering.
  • D. Use an Amazon SageMaker Data Wrangler data flow to create and modify a data preparation pipeline.Manually add the feature scores.
Answer: B
Explanation:
To evaluate the contribution of each feature before model training, feature importance scores are a primary tool. In Amazon SageMaker Data Wrangler, a Quick Model can be used to rapidly generate a model on the dataset and return feature importance scores. These scores indicate how much each feature influences the target variable.
"Use Quick Model to generate feature importance scores quickly to help understand which features contribute the most to predictions. These scores range from 0 to 1, with higher values indicating higher contribution." This allows ML engineers to assess feature relevance without full-scale model development, thus saving time and effort in the feature selection phase of the pipeline.

NEW QUESTION # 213
A machine learning engineer is building a bird classification model. The engineer randomly separates a dataset into a training dataset and a validation dataset. During the training phase, the model achieves very high accuracy. However, the model did not generalize well during validation of the validation dataset. The engineer realizes that the original dataset was imbalanced.
What should the engineer do to improve the validation accuracy of the model?
  • A. Use a smaller, randomly sampled version of the training dataset.
  • B. Acquire additional data about the majority classes in the original dataset.
  • C. Perform stratified sampling on the original dataset.
  • D. Perform systematic sampling on the original dataset.
Answer: C
Explanation:
Stratified sampling is a technique that preserves the class distribution of the original dataset when creating a smaller or split dataset. This means that the proportion of examples from each class in the original dataset is maintained in the smaller or split dataset. Stratified sampling can help improve the validation accuracy of the model by ensuring that the validation dataset is representative of the original dataset and not biased towards any class. This can reduce the variance and overfitting of the model and increase its generalization ability.
Stratified sampling can be applied to both oversampling and undersampling methods, depending on whether the goal is to increase or decrease the size of the dataset.
The other options are not effective ways to improve the validation accuracy of the model. Acquiring additional data about the majority classes in the original dataset will only increase the imbalance and make the model more biased towards the majority classes. Using a smaller, randomly sampled version of the training dataset will not guarantee that the class distribution is preserved and may result in losing important information from the minority classes. Performing systematic sampling on the original dataset will also not ensure that the class distribution is preserved and may introduce sampling bias if the original dataset is ordered or grouped by class.

NEW QUESTION # 214
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 the EC2 instances within the customer account, but they run outside of VPCs.
  • 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 EC2 instances running within AWS service accounts.
Answer: D
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 # 215
......
You may want to know our different versions of MLS-C01 exam questions. Firstly, PDF version is easy to read and print. Secondly software version simulates the real MLS-C01 actual test guide, but it can only run on Windows operating system. Thirdly, online version supports for any electronic equipment and also supports offline use. For the first time, you need to open MLS-C01 Exam Questions in online environment, and then you can use it offline. All in all, helping our candidates to pass the exam successfully is what we always looking for. Our MLS-C01 actual test guide is your best choice.
New MLS-C01 Study Guide: https://www.torrentexam.com/MLS-C01-exam-latest-torrent.html
2026 Latest TorrentExam MLS-C01 PDF Dumps and MLS-C01 Exam Engine Free Share: https://drive.google.com/open?id=15d5xvNgW8BdHiYIgg5cp2al5KHPSsF5R
https://www.actualtestsquiz.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