LATEST MLS-C01 EXAM PRACTICE 100% PASS | PROFESSIONAL MLS-C01: AWS CERTIFIED MACHINE LEARNING - SPECIALTY 100% PASS

Latest MLS-C01 Exam Practice 100% Pass | Professional MLS-C01: AWS Certified Machine Learning - Specialty 100% Pass

Latest MLS-C01 Exam Practice 100% Pass | Professional MLS-C01: AWS Certified Machine Learning - Specialty 100% Pass

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Amazon MLS-C01 Exam covers a wide range of topics related to machine learning, including data preparation, feature engineering, model selection, model training, and model deployment. It also assesses the candidate's understanding of AWS services such as Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, and Amazon Translate, which are commonly used for machine learning applications.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q183-Q188):

NEW QUESTION # 183
A Machine Learning Specialist deployed a model that provides product recommendations on a company's website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less. The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago.
Which method should the Specialist try to improve model performance?

  • A. The model needs to be completely re-engineered because it is unable to handle product inventory changes.
  • B. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
  • C. The model should be periodically retrained using the original training data plus new data as product inventory changes.
  • D. The model's hyperparameters should be periodically updated to prevent drift.

Answer: C


NEW QUESTION # 184
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
* Real-time analytics
* Interactive analytics of historical data
* Clickstream analytics
* Product recommendations
Which services should the Specialist use?

  • A. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
  • B. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
  • C. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
  • D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

Answer: B

Explanation:
The best services to use for building a data ingestion solution for the company's Amazon S3-based data lake are:
* AWS Glue as the data catalog: AWS Glue is a fully managed extract, transform, and load (ETL) service that can discover, crawl, and catalog data from various sources and formats, and make it available for analysis. AWS Glue can also generate ETL code in Python or Scala to transform, enrich, and join data using AWS Glue Data Catalog as the metadata repository. AWS Glue Data Catalog is a central metadata store that integrates with Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum, allowing users to create a unified view of their data across various sources and formats.
* Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights: Amazon Kinesis Data Streams is a service that enables users to collect, process, and analyze real-time streaming data at any scale. Users can create data streams that can capture data from various sources, such as web and mobile applications, IoT devices, and social media platforms. Amazon Kinesis Data Analytics is a service that allows users to analyze streaming data using standard SQL queries or Apache Flink applications. Users can create real-time dashboards, metrics, and alerts based on the streaming data analysis results.
* Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics: Amazon Kinesis Data Firehose is a service that enables users to load streaming data into data lakes, data stores, and analytics services. Users can configure Kinesis Data Firehose to automatically deliver data to various destinations, such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and third-party solutions. For clickstream analytics, users can use Kinesis Data Firehose to deliver data to Amazon OpenSearch Service, a fully managed service that offers search and analytics capabilities for log data.
Users can use Amazon OpenSearch Service to perform interactive analysis and visualization of clickstream data using Kibana, an open-source tool that is integrated with Amazon OpenSearch Service.
* Amazon EMR to generate personalized product recommendations: Amazon EMR is a service that enables users to run distributed data processing frameworks, such as Apache Spark, Apache Hadoop, and Apache Hive, on scalable clusters of EC2 instances. Users can use Amazon EMR to perform advanced analytics, such as machine learning, on large and complex datasets stored in Amazon S3 or other sources. For product recommendations, users can use Amazon EMR to run Spark MLlib, a library that provides scalable machine learning algorithms, such as collaborative filtering, to generate personalized recommendations based on user behavior and preferences.
References:
* AWS Glue - Fully Managed ETL Service
* Amazon Kinesis - Data Streaming Service
* Amazon OpenSearch Service - Managed OpenSearch Service
* Amazon EMR - Managed Hadoop Framework


NEW QUESTION # 185
A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city as this is a prototype, only daily data from the last year is available Which model is MOST likely to provide the best results in Amazon SageMaker?

  • A. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.
  • B. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
  • C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
  • D. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.

Answer: B

Explanation:
Explanation
The Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm is a supervised learning algorithm that can perform both classification and regression tasks. It can also handle time series data, such as the air quality data in this case. The kNN algorithm works by finding the k most similar instances in the training data to a given query instance, and then predicting the output based on the average or majority of the outputs of the k nearest neighbors. The kNN algorithm can be configured to use different distance metrics, such as Euclidean or cosine, to measure the similarity between instances. To use the kNN algorithm on the single time series consisting of the full year of data, the Machine Learning Specialist needs to set the predictor_type parameter to regressor, as the output variable (air quality in parts per million of contaminates) is a continuous value. The kNN algorithm can then forecast the air quality for the next 2 days by finding the k most similar days in the past year and averaging their air quality values.
References:
Amazon SageMaker k-Nearest-Neighbors (kNN) Algorithm - Amazon SageMaker Time Series Forecasting using k-Nearest Neighbors (kNN) in Python | by ...
Time Series Forecasting with k-Nearest Neighbors | by Nishant Malik ...


NEW QUESTION # 186
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. Increase the amount of regularization. Use fewer feature combinations.
  • B. Increase the number of passes on the existing training data. Perform more hyperparameter tuning.
  • C. Use fewer feature combinations. Decrease the number of numeric attribute bins.
  • D. Decrease the amount of training data examples. Reduce the number of passes on the existing training data.
  • 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 # 187
The displayed graph is from a forecasting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

  • A. The model predicts the seasonality well, but not the trend.
  • B. The model predicts both the trend and the seasonality well
  • C. The model does not predict the trend or the seasonality well.
  • D. The model predicts the trend well, but not the seasonality.

Answer: C


NEW QUESTION # 188
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