EXAM MLS-C01 FORUM - MLS-C01 RELIABLE EXAM BOOTCAMP

Exam MLS-C01 Forum - MLS-C01 Reliable Exam Bootcamp

Exam MLS-C01 Forum - MLS-C01 Reliable Exam Bootcamp

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Tags: Exam MLS-C01 Forum, MLS-C01 Reliable Exam Bootcamp, MLS-C01 Valid Test Duration, Latest MLS-C01 Braindumps, MLS-C01 Actual Test Pdf

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To be eligible for the Amazon MLS-C01 exam, candidates must have a minimum of one year of experience using AWS services for machine learning solutions. They should also have a strong understanding of machine learning concepts and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning. Additionally, candidates should have experience with AWS services such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend. MLS-C01 exam consists of 65 multiple-choice and multiple-response questions, and the time limit for completing it is 180 minutes. Upon successful completion of the exam, candidates will receive the AWS Certified Machine Learning - Specialty certification.

The Amazon MLS-C01 Exam covers a wide range of topics, including data pre-processing, feature engineering, model selection and evaluation, model training and optimization, deployment and monitoring, and ethics and fairness in machine learning. MLS-C01 exam also tests the candidate's ability to use AWS tools and services such as Amazon SageMaker, Amazon S3, Amazon EC2, and Amazon EMR.

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Quiz MLS-C01 - Trustable Exam AWS Certified Machine Learning - Specialty Forum

In today's competitive Amazon industry, only the brightest and most qualified candidates are hired for high-paying positions. Obtaining MLS-C01 certification is a wonderful approach to be successful because it can draw in prospects and convince companies that you are the finest in your field. Pass the AWS Certified Machine Learning - Specialty to establish your expertise in your field and receive certification. However, passing the AWS Certified Machine Learning - Specialty MLS-C01 Exam is challenging.

Achieving the AWS Certified Machine Learning - Specialty certification can help individuals advance their careers in the field of machine learning and increase their earning potential. AWS Certified Machine Learning - Specialty certification is recognized by industry leaders and can open up new opportunities for professionals in various industries, including healthcare, finance, and retail, among others.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q255-Q260):

NEW QUESTION # 255
A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3 The source systems send data in CSV format in real lime The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3 Which solution takes the LEAST effort to implement?

  • A. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
  • B. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.
  • C. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.
  • D. Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet

Answer: A

Explanation:
https://medium.com/searce/convert-csv-json-files-to-apache-parquet-using-aws-glue-a760d177b45f
https://github.com/ecloudvalley/Building-a-Data-Lake-with-AWS-Glue-and-Amazon-S3


NEW QUESTION # 256
A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test dat a. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.

Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

  • A. Lower the max_depth parameter value.
  • B. Increase the max_depth parameter value.
  • C. Lower the min_child_weight parameter value.
  • D. Update the objective to binary:logistic.

Answer: A

Explanation:
Overfitting occurs when a model performs well on the training data but poorly on the test data. This is often because the model has learned the training data too well and is not able to generalize to new data. To avoid overfitting, the Machine Learning Specialist should lower the max_depth parameter value. This will reduce the complexity of the model and make it less likely to overfit. According to the XGBoost documentation1, the max_depth parameter controls the maximum depth of a tree and lower values can help prevent overfitting. The documentation also suggests other ways to control overfitting, such as adding randomness, using regularization, and using early stopping1. References:
XGBoost Parameters


NEW QUESTION # 257
A beauty supply store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to generate a report of hourly visitors from the recordings. The report should group visitors by hair style and hair color.
Which solution will meet these requirements with the LEAST amount of effort?

  • A. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
  • B. Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair color.
  • C. Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
  • D. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair.

Answer: A

Explanation:
The solution that will meet the requirements with the least amount of effort is to use a semantic segmentation algorithm to identify a visitor's hair in video frames, and pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color. This solution can leverage the existing Amazon SageMaker algorithms and frameworks to perform the tasks of hair segmentation and classification.
Semantic segmentation is a computer vision technique that assigns a class label to every pixel in an image, such that pixels with the same label share certain characteristics. Semantic segmentation can be used to identify and isolate different objects or regions in an image, such as a visitor's hair in a video frame. Amazon SageMaker provides a built-in semantic segmentation algorithm that can train and deploy models for semantic segmentation tasks. The algorithm supports three state-of-the-art network architectures: Fully Convolutional Network (FCN), Pyramid Scene Parsing Network (PSP), and DeepLab v3. The algorithm can also use pre- trained or randomly initialized ResNet-50 or ResNet-101 as the backbone network. The algorithm can be trained using P2/P3 type Amazon EC2 instances in single machine configurations1.
ResNet-50 is a convolutional neural network that is 50 layers deep and can classify images into 1000 object categories. ResNet-50 is trained on more than a million images from the ImageNet database and can achieve high accuracy on various image recognition tasks. ResNet-50 can be used to determine hair style and hair color from the segmented hair regions in the video frames. Amazon SageMaker provides a built-in image classification algorithm that can use ResNet-50 as the network architecture. The algorithm can also perform transfer learning by fine-tuning the pre-trained ResNet-50 model with new data. The algorithm can be trained using P2/P3 type Amazon EC2 instances in single or multiple machine configurations2.
The other options are either less effective or more complex to implement. Using an object detection algorithm to identify a visitor's hair in video frames would not segment the hair at the pixel level, but only draw bounding boxes around the hair regions. This could result in inaccurate or incomplete hair segmentation, especially if the hair is occluded or has irregular shapes. Using an XGBoost algorithm to determine hair style and hair color would require transforming the segmented hair images into numerical features, which could lose some information or introduce noise. XGBoost is also not designed for image classification tasks, and may not achieve high accuracy or performance.
1: Semantic Segmentation Algorithm - Amazon SageMaker
2: Image Classification Algorithm - Amazon SageMaker


NEW QUESTION # 258
A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE)

  • A. Download the AWS SDK for the Spark environment
  • B. Convert the DataFrame object to a CSV file, and use the CSV file as input for obtaining inferences from SageMaker.
  • C. Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker
  • D. Use the appropriate estimator from the SageMaker Spark Library to train a model.
  • E. Install the SageMaker Spark library in the Spark environment.
  • F. Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket.

Answer: C,D,E

Explanation:
Explanation
The SageMaker Spark library is a library that enables Apache Spark applications to integrate with Amazon SageMaker for training and hosting machine learning models. The library provides several features, such as:
Estimators: Classes that allow Spark users to train Amazon SageMaker models and host them on Amazon SageMaker endpoints using the Spark MLlib Pipelines API. The library supports various built-in algorithms, such as linear learner, XGBoost, K-means, etc., as well as custom algorithms using Docker containers.
Model classes: Classes that wrap Amazon SageMaker models in a Spark MLlib Model abstraction. This allows Spark users to use Amazon SageMaker endpoints for inference within Spark applications.
Data sources: Classes that allow Spark users to read data from Amazon S3 using the Spark Data Sources API. The library supports various data formats, such as CSV, LibSVM, RecordIO, etc.
To integrate the Spark application with SageMaker, the Machine Learning Specialist should do the following:
Install the SageMaker Spark library in the Spark environment. This can be done by using Maven, pip, or downloading the JAR file from GitHub.
Use the appropriate estimator from the SageMaker Spark Library to train a model. For example, to train a linear learner model, the Specialist can use the following code:

Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker. For example, to get predictions for a test DataFrame, the Specialist can use the following code:

References:
[SageMaker Spark]: A documentation page that introduces the SageMaker Spark library and its features.
[SageMaker Spark GitHub Repository]: A GitHub repository that contains the source code, examples, and installation instructions for the SageMaker Spark library.


NEW QUESTION # 259
A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.
Which storage option provides the most processing flexibility and will allow access control with IAM?

  • A. Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
  • B. Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
  • C. Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.
  • D. Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.

Answer: D

Explanation:
The best storage option for the trucking company is to use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies. A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Amazon S3 is the ideal choice for building a data lake because it offers high durability, scalability, availability, and security. You can store any type of data in Amazon S3, such as images, videos, audio, text, etc. You can also use AWS services such as Amazon Rekognition, Amazon SageMaker, and Amazon EMR to analyze and process the data in the data lake. To ensure the data is only accessible to specific IAM users, you can use bucket policies to grant or deny access to the S3 buckets based on the IAM user's identity or role. Bucket policies are JSON documents that specify the permissions for the bucket and the objects in it. You can use conditions to restrict access based on various factors, such as IP address, time, source, etc. By using bucket policies, you can control who can access the data in the data lake and what actions they can perform on it.
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Build a Data Lake Foundation with Amazon S3 AWS Machine Learning Training - Using Bucket Policies and User Policies


NEW QUESTION # 260
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