PASS4SURE AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY EXAM PREP | EXAM AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY QUESTIONS FEE

Pass4sure AWS-Certified-Machine-Learning-Specialty Exam Prep | Exam AWS-Certified-Machine-Learning-Specialty Questions Fee

Pass4sure AWS-Certified-Machine-Learning-Specialty Exam Prep | Exam AWS-Certified-Machine-Learning-Specialty Questions Fee

Blog Article

Tags: Pass4sure AWS-Certified-Machine-Learning-Specialty Exam Prep, Exam AWS-Certified-Machine-Learning-Specialty Questions Fee, Valid Braindumps AWS-Certified-Machine-Learning-Specialty Ppt, AWS-Certified-Machine-Learning-Specialty Exam Lab Questions, AWS-Certified-Machine-Learning-Specialty Real Exam

2025 Latest RealVCE AWS-Certified-Machine-Learning-Specialty PDF Dumps and AWS-Certified-Machine-Learning-Specialty Exam Engine Free Share: https://drive.google.com/open?id=1G1QW5QLTpVC7cyzYqAgaF6jpZPuFO4jc

Now you can pass AWS Certified Machine Learning - Specialty exam without going through any hassle. You can only focus on AWS-Certified-Machine-Learning-Specialty exam dumps provided by the RealVCE, and you will be able to pass the AWS Certified Machine Learning - Specialty test in the first attempt. We provide high quality and easy to understand AWS-Certified-Machine-Learning-Specialty pdf dumps with verified Amazon AWS-Certified-Machine-Learning-Specialty for all the professionals who are looking to pass the AWS-Certified-Machine-Learning-Specialty exam in the first attempt. The AWS-Certified-Machine-Learning-Specialty training material package includes latest AWS-Certified-Machine-Learning-Specialty PDF questions and practice test software that will help you to pass the AWS-Certified-Machine-Learning-Specialty exam.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Certification Exam is a highly specialized certification program that focuses on machine learning technologies and techniques in the Amazon Web Services (AWS) ecosystem. AWS Certified Machine Learning - Specialty certification program is designed to assess the skills and knowledge of professionals who work with AWS machine learning technologies and tools, and who wish to demonstrate their expertise in this area.

Earning the AWS Certified Machine Learning - Specialty certification demonstrates to employers and colleagues that you have the skills and knowledge needed to design and deploy machine learning models on the AWS platform. It can help you stand out in a competitive job market and increase your earning potential.

The AWS Certified Machine Learning - Specialty certification exam is ideal for professionals who are looking to advance their careers in the field of machine learning and artificial intelligence. It is a great way to showcase your skills and expertise to potential employers and clients, and to demonstrate your commitment to staying up-to-date with the latest developments in this rapidly evolving field. Additionally, AWS certification exams are recognized globally, which means that earning this certification can help you land new job opportunities in different countries and regions.

>> Pass4sure AWS-Certified-Machine-Learning-Specialty Exam Prep <<

Exam Amazon AWS-Certified-Machine-Learning-Specialty Questions Fee & Valid Braindumps AWS-Certified-Machine-Learning-Specialty Ppt

Are you still worried about the complex AWS-Certified-Machine-Learning-Specialty exam? Do not be afraid. AWS-Certified-Machine-Learning-Specialty exam dumps and answers from our RealVCE site are all created by the IT talents with more than 10 years'certification experience. Moreover, AWS-Certified-Machine-Learning-Specialty Exam Dumps and answers are the most accuracy and the newest inspection goods.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q279-Q284):

NEW QUESTION # 279
An agricultural company is interested in using machine learning to detect specific types of weeds in a 100-acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10 X 10 grids. The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks.
The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints. The model will perform real-time inferencing using the images captured by the cameras.
Which approach should a Machine Learning Specialist take to obtain accurate predictions?

  • A. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
  • B. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.
  • C. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
  • D. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.

Answer: D

Explanation:
The problem of detecting specific types of weeds and their location within the field is an example of object detection, which is a type of machine learning model that identifies and localizes objects in an image. Amazon SageMaker provides a built-in object detection algorithm that uses a single-shot multibox detector (SSD) to perform real-time inference on streaming images. The SSD algorithm can handle multiple objects of varying sizes and scales in an image, and generate bounding boxes and scores for each object category. Therefore, option C is the best approach to obtain accurate predictions.
Option A is incorrect because image classification is a type of machine learning model that assigns a label to an image based on predefined categories. Image classification is not suitable for localizing objects within an image, as it does not provide bounding boxes or scores for each object. Option B is incorrect because Apache Parquet is a columnar storage format that is optimized for analytical queries. Apache Parquet is not suitable for storing images, as it does not preserve the spatial information of the pixels. Option D is incorrect because it combines the wrong format (Apache Parquet) and the wrong algorithm (image classification) for the given problem, as explained in options A and B.
References:
Object Detection algorithm now available in Amazon SageMaker
Image classification and object detection using Amazon Rekognition Custom Labels and Amazon SageMaker JumpStart Object Detection with Amazon SageMaker - W3Schools aws-samples/amazon-sagemaker-tensorflow-object-detection-api


NEW QUESTION # 280
A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?

  • A. Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.
  • B. Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.
  • C. Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.
  • D. Randomly select 10% of the users. Split off all interaction data from these users for the test set.

Answer: D

Explanation:
The best way to split the dataset into a training and test set for this use case is to randomly select 10% of the users and split off all interaction data from these users for the test set. This is because the company relies on a steady stream of new customers, so the test set should reflect the behavior of new customers who have not been seen by the model before. The other options are not suitable because they either mix old and new customers in the test set (A and B), or they bias the test set towards users with less interaction data .
References:
* Amazon SageMaker Developer Guide: Train and Test Datasets
* Amazon Personalize Developer Guide: Preparing and Importing Data


NEW QUESTION # 281
A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents How should a Machine Learning Specialist address this issue for future documents'?

  • A. Use Amazon Lex to preprocess the text files for pronunciation
  • B. Create an appropriate pronunciation lexicon.
  • C. Convert current documents to SSML with pronunciation tags
  • D. Output speech marks to guide in pronunciation

Answer: C


NEW QUESTION # 282
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

  • A. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
  • B. Hyperparameters in a JSON array as documented for the algorithm used.
  • C. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
  • D. The output path specifying where on an Amazon S3 bucket the trained model will persist.
  • E. The validation channel identifying the location of validation data on an Amazon S3 bucket.
  • F. The training channel identifying the location of training data on an Amazon S3 bucket.

Answer: C,D,F

Explanation:
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, the common parameters that must be specified are:
The training channel identifying the location of training data on an Amazon S3 bucket. This parameter tells SageMaker where to find the input data for the algorithm and what format it is in. For example, TrainingInputMode: File means that the input data is in files stored in S3.
The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users. This parameter grants SageMaker the necessary permissions to access the S3 buckets, ECR repositories, and other AWS resources needed for the training job. For example, RoleArn: arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-20200303T150948 means that SageMaker will use the specified role to run the training job.
The output path specifying where on an Amazon S3 bucket the trained model will persist. This parameter tells SageMaker where to save the model artifacts, such as the model weights and parameters, after the training job is completed. For example, OutputDataConfig: {S3OutputPath: s3://my-bucket/my-training-job} means that SageMaker will store the model artifacts in the specified S3 location.
The validation channel identifying the location of validation data on an Amazon S3 bucket is an optional parameter that can be used to provide a separate dataset for evaluating the model performance during the training process. This parameter is not required for all algorithms and can be omitted if the validation data is not available or not needed.
The hyperparameters in a JSON array as documented for the algorithm used is another optional parameter that can be used to customize the behavior and performance of the algorithm. This parameter is specific to each algorithm and can be used to tune the model accuracy, speed, complexity, and other aspects. For example, HyperParameters: {num_round: "10", objective: "binary:logistic"} means that the XGBoost algorithm will use 10 boosting rounds and the logistic loss function for binary classification.
The Amazon EC2 instance class specifying whether training will be run using CPU or GPU is not a parameter that is specified when submitting a training job using a built-in algorithm. Instead, this parameter is specified when creating a training instance, which is a containerized environment that runs the training code and algorithm. For example, ResourceConfig: {InstanceType: ml.m5.xlarge, InstanceCount: 1, VolumeSizeInGB: 10} means that SageMaker will use one m5.xlarge instance with 10 GB of storage for the training instance.
References:
Train a Model with Amazon SageMaker
Use Amazon SageMaker Built-in Algorithms or Pre-trained Models
CreateTrainingJob - Amazon SageMaker Service


NEW QUESTION # 283
A machine learning (ML) specialist is building a credit score model for a financial institution. The ML specialist has collected data for the previous 3 years of transactions and third-party metadata that is related to the transactions.
After the ML specialist builds the initial model, the ML specialist discovers that the model has low accuracy for both the training data and the test data. The ML specialist needs to improve the accuracy of the model.
Which solutions will meet this requirement? (Select TWO.)

  • A. Decrease the amount of training data examples. Reduce the number of passes on the existing training data.
  • B. Increase the number of passes on the existing training data. Perform more hyperparameter tuning.
  • C. Increase the amount of regularization. Use fewer feature combinations.
  • D. Use fewer feature combinations. Decrease the number of numeric attribute bins.
  • E. Add new domain-specific features. Use more complex models.

Answer: B,E

Explanation:
For a model with low accuracy on both training and testing datasets, the following two strategies are effective:
* Increase the number of passes and perform hyperparameter tuning: This approach allows the model to better learn from the existing data and improve performance through optimized hyperparameters.
* Add domain-specific features and use more complex models: Adding relevant features that capture additional information from domain knowledge and using more complex model architectures can help the model capture patterns better, potentially improving accuracy.
Options B, D, and E would either reduce feature complexity or training data volume, which is less likely to improve performance when accuracy is low on both training and testing sets.


NEW QUESTION # 284
......

We put high emphasis on the protection of our customers’ personal data and fight against criminal actson our AWS-Certified-Machine-Learning-Specialty exam questions. Our AWS-Certified-Machine-Learning-Specialty preparation exam is consisted of a team of professional experts and technical staff, which means that you can trust our security system with whole-heart. As for your concern about the network virus invasion, AWS-Certified-Machine-Learning-Specialty Learning Materials guarantee that our purchasing channel is absolutely worthy of your trust.

Exam AWS-Certified-Machine-Learning-Specialty Questions Fee: https://www.realvce.com/AWS-Certified-Machine-Learning-Specialty_free-dumps.html

BTW, DOWNLOAD part of RealVCE AWS-Certified-Machine-Learning-Specialty dumps from Cloud Storage: https://drive.google.com/open?id=1G1QW5QLTpVC7cyzYqAgaF6jpZPuFO4jc

Report this page