100% PASS QUIZ 2025 AMAZON AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY: HIGH HIT-RATE AWS CERTIFIED MACHINE LEARNING - SPECIALTY LATEST TEST PREP

100% Pass Quiz 2025 Amazon AWS-Certified-Machine-Learning-Specialty: High Hit-Rate AWS Certified Machine Learning - Specialty Latest Test Prep

100% Pass Quiz 2025 Amazon AWS-Certified-Machine-Learning-Specialty: High Hit-Rate AWS Certified Machine Learning - Specialty Latest Test Prep

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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) exam is a certification program designed for professionals who want to demonstrate their expertise in the field of machine learning. AWS-Certified-Machine-Learning-Specialty exam is intended to validate the knowledge and skills of candidates in building, training, and deploying machine learning models on the Amazon Web Services (AWS) platform.

The MLS-C01 exam is a specialty certification that focuses on machine learning concepts and practices. It is designed for professionals who have a background in data science, computer science, or software engineering and want to specialize in machine learning. AWS-Certified-Machine-Learning-Specialty Exam is designed to test the candidate's ability to apply machine learning algorithms to solve real-world problems and build scalable solutions that can handle large data sets.

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Certification AWS-Certified-Machine-Learning-Specialty Cost | AWS-Certified-Machine-Learning-Specialty Actual Exams

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Amazon MLS-C01 Exam is a highly respected certification that validates the skills and knowledge of individuals who work with machine learning technologies on the AWS platform. By passing AWS-Certified-Machine-Learning-Specialty Exam, candidates can demonstrate their expertise in machine learning, as well as their ability to design, deploy, and maintain machine learning solutions on AWS.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q196-Q201):

NEW QUESTION # 196
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.
What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

  • A. L1 norm
  • B. Receiver operating characteristic (ROC) curve
  • C. Root Mean Square Error (RMSE)
  • D. Misclassification rate

Answer: B

Explanation:
https://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.html


NEW QUESTION # 197
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. Install the SageMaker Spark library in the Spark environment.
  • C. Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker
  • D. Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket.
  • E. Use the appropriate estimator from the SageMaker Spark Library to train a model.
  • F. Convert the DataFrame object to a CSV file, and use the CSV file as input for obtaining inferences from SageMaker.

Answer: B,C,E

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 # 198
A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.
What should the company do to reduce the processing time of loan applications?

  • A. Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I).
    Perform a manual review on those words before performing a business validation.
  • B. Use Amazon Rekognition's feature to detect text in an image to extract the data from scanned images. Use this information to process the loan applications.
  • C. Use an Amazon Textract synchronous operation instead of an asynchronous operation.
  • D. Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth.
    Perform a manual review on those words before performing a business validation.

Answer: A


NEW QUESTION # 199
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 Which approach will provide the information required for further analysis?

  • A. Use Amazon Translate with the transcribed files to train and build a model for the key topics
  • B. Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics
  • C. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics
  • D. Use Amazon Comprehend with the transcribed files to build the key topics

Answer: D

Explanation:
Explanation
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. It can analyze text documents and identify the key topics, entities, sentiments, languages, and more. In this case, Amazon Comprehend can be used with the transcribed files from Amazon Transcribe to extract the main topics that are being discussed by the call operators. This can help to understand the common issues and concerns of the customers, and provide insights for further analysis and improvement. References:
Amazon Comprehend - Amazon Web Services
AWS Certified Machine Learning - Specialty Sample Questions


NEW QUESTION # 200
An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

  • A. Last observation carried forward
  • B. Listwise deletion
  • C. Multiple imputation
  • D. Mean substitution

Answer: C

Explanation:
Explanation
Multiple imputation is a technique that uses machine learning to generate multiple plausible values for each missing value in a dataset, based on the observed data and the relationships among the variables. Multiple imputation preserves the integrity of the dataset by accounting for the uncertainty and variability of the missing data, and avoids the bias and loss of information that may result from other methods, such as listwise deletion, last observation carried forward, or mean substitution. Multiple imputation can improve the accuracy and validity of statistical analysis and machine learning models that use the imputed dataset. References:
Managing missing values in your target and related datasets with automated imputation support in Amazon Forecast Imputation by feature importance (IBFI): A methodology to impute missing data in large datasets Multiple Imputation by Chained Equations (MICE) Explained


NEW QUESTION # 201
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