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[Hardware] Amazon MLS-C01 Exam Actual Tests | MLS-C01 Reliable Test Simulator

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【Hardware】 Amazon MLS-C01 Exam Actual Tests | MLS-C01 Reliable Test Simulator

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By keeping customer satisfaction in mind, BraindumpStudy offers you a free demo of the AWS Certified Machine Learning - Specialty (MLS-C01) exam questions. As a result, it helps you to evaluate the AWS Certified Machine Learning - Specialty (MLS-C01) exam dumps before making a purchase. BraindumpStudy is steadfast in its commitment to helping you pass the Amazon in MLS-C01 Exam. A full refund guarantee (terms and conditions apply) offered by BraindumpStudy will save you from fear of money loss.
The AWS Certified Machine Learning - Specialty certification is highly valued in the industry and is recognized as a benchmark for expertise in the field of machine learning. It is a great way for individuals to demonstrate their proficiency in using AWS services to build and deploy machine learning models. AWS Certified Machine Learning - Specialty certification also helps individuals stand out in a competitive job market and opens up new opportunities for career growth in fields such as data science, artificial intelligence, and machine learning.
Difficulty in writing AWS Certified Machine Learning - SpecialtyIt's been observed that the Amazon MLS exam is one of the toughest exam conducted by the AWS. The candidate needs to have dedicationa and make continued effort in right direction. Proper study plan and preparation for the exam would help you to get through with this exam.
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Amazon MLS-C01 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Exploratory Data Analysis: This topic covers sanitizing and preparing data for modeling and performing feature engineering. Additionally, it discusses analyzing and visualizing data for ML.
Topic 2
  • Machine Learning Implementation and Operations: Building ML solutions for performance, availability, scalability, resiliency, and fault tolerance is discussed in this topic. It also focuses on suitable ML services and features for a given problem. Lastly, the topic delves into applying basic AWS security practices to ML solutions and deploying and operationalizing ML solutions.
Topic 3
  • Data Engineering: It discusses creating data repositories for ML, identifying and implementing a data ingestion solution. Lastly, the topic delves into identifying and implementing a data transformation solution.
Topic 4
  • Modeling: The topic of modeling deals with framing business problems as ML problems, choosing the suitable model(s) for a given ML problem, training ML models. It also discusses hyperparameter optimization and evaluation of ML models.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q20-Q25):NEW QUESTION # 20
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)
  • A. Decrease dropout.
  • B. Increase feature combinations.
  • C. Decrease feature combinations.
  • D. Increase dropout.
  • E. Increase regularization.
  • F. Decrease regularization.
Answer: A,D,E

NEW QUESTION # 21
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.
What technique should be used to convert this column to binary values.

  • A. Binarization
  • B. Tokenization
  • C. One-hot encoding
  • D. Normalization transformation
Answer: C

NEW QUESTION # 22
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?
  • A. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
  • B. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
  • C. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
  • D. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
Answer: B

NEW QUESTION # 23
A media company with a very large archive of unlabeled images, text, audio, and video footage wishes to index its assets to allow rapid identification of relevant content by the Research team. The company wants to use machine learning to accelerate the efforts of its in-house researchers who have limited machine learning expertise.
Which is the FASTEST route to index the assets?
  • A. Use the AWS Deep Learning AMI and Amazon EC2 GPU instances to create custom models for audio transcription and topic modeling, and use object detection to tag data into distinct categories/classes.
  • B. Use Amazon Transcribe to convert speech to text. Use the Amazon SageMaker Neural Topic Model (NTM) and Object Detection algorithms to tag data into distinct categories/classes.
  • C. Create a set of Amazon Mechanical Turk Human Intelligence Tasks to label all footage.
  • D. Use Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinct categories/classes.
Answer: D
Explanation:
Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe are AWS machine learning services that can analyze and extract metadata from images, text, audio, and video content. These services are easy to use, scalable, and do not require any machine learning expertise. They can help the media company to quickly index its assets and enable rapid identification of relevant content by the research team. Using these services is the fastest route to index the assets, compared to the other options that involve human intervention, custom model development, or additional steps. References:
AWS Media Intelligence Solutions
AWS Machine Learning Services
The Best Services For Running Machine Learning Models On AWS

NEW QUESTION # 24
A company has video feeds and images of a subway train station. The company wants to create a deep learning model that will alert the station manager if any passenger crosses the yellow safety line when there is no train in the station. The alert will be based on the video feeds. The company wants the model to detect the yellow line, the passengers who cross the yellow line, and the trains in the video feeds. This task requires labeling. The video data must remain confidential.
A data scientist creates a bounding box to label the sample data and uses an object detection model. However, the object detection model cannot clearly demarcate the yellow line, the passengers who cross the yellow line, and the trains.
Which labeling approach will help the company improve this model?
  • A. Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a workforce with a third-party AWS Marketplace vendor. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.
  • B. Use an Amazon SageMaker Ground Truth semantic segmentation labeling task. Use a private workforce as the labeling workforce.
  • C. Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a private workforce. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.
  • D. Use an Amazon SageMaker Ground Truth object detection labeling task. Use Amazon Mechanical Turk as the labeling workforce.
Answer: D

NEW QUESTION # 25
......
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