AMAZON MLS-C01 PDF QUESTIONS - ENSURE YOUR SUCCESS IN EXAM

Amazon MLS-C01 PDF Questions - Ensure Your Success In Exam

Amazon MLS-C01 PDF Questions - Ensure Your Success In Exam

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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam designed for individuals who want to demonstrate their expertise in machine learning on the AWS platform. MLS-C01 exam is intended for professionals who have experience using AWS services for designing, building, and deploying machine learning solutions. AWS Certified Machine Learning - Specialty certification exam validates the candidate's ability to design, implement, and deploy machine learning models using AWS services.

Amazon MLS-C01 Exam is one of the most sought-after certifications in the field of machine learning. AWS Certified Machine Learning - Specialty certification validates the candidate's knowledge and skills in designing, implementing, deploying, and maintaining machine learning solutions using Amazon Web Services (AWS). MLS-C01 exam is intended for individuals who have experience with machine learning and AWS, and are looking to take their expertise to the next level.

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The Amazon MLS-C01 exam consists of 65 multiple-choice and multiple-response questions, and candidates are given 180 minutes to complete the exam. MLS-C01 Exam Fee is $300, and candidates must achieve a passing score of 750 out of 1000 to earn their certification.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q210-Q215):

NEW QUESTION # 210
A large consumer goods manufacturer has the following products on sale
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched Which solution should a Machine Learning Specialist apply?

  • A. Train a custom XGBoost model to forecast demand for the new product
  • B. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
  • C. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
  • D. Train a custom ARIMA model to forecast demand for the new product.

Answer: C

Explanation:
The company wants to predict the demand for a new product that will soon be launched, based on the sales history of similar products. This is a time series forecasting problem, which requires a machine learning algorithm that can learn from historical data and generate future predictions.
One of the most suitable solutions for this problem is to use the Amazon SageMaker DeepAR algorithm, which is a supervised learning algorithm for forecasting scalar time series using recurrent neural networks (RNN). DeepAR can handle multiple related time series, such as the sales of different products, and learn a global model that captures the common patterns and trends across the time series. DeepAR can also generate probabilistic forecasts that provide confidence intervals and quantify the uncertainty of the predictions.
DeepAR can outperform traditional forecasting methods, such as ARIMA, especially when the dataset contains hundreds or thousands of related time series. DeepAR can also use the trained model to forecast the demand for new products that are similar to the ones it has been trained on, by using the categorical features that encode the product attributes. For example, the company can use the product type, brand, flavor, size, and price as categorical features to group the products and learn the typical behavior for each group.
Therefore, the Machine Learning Specialist should apply the Amazon SageMaker DeepAR algorithm to forecast the demand for the new product, by using the sales history of the existing products as the training dataset, and the product attributes as the categorical features.
DeepAR Forecasting Algorithm - Amazon SageMaker
Now available in Amazon SageMaker: DeepAR algorithm for more accurate time series forecasting


NEW QUESTION # 211
A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.
The data scientists are using Amazon Forecast to generate the forecasts.
Which algorithm in Forecast should the data scientists use to meet these requirements?

  • A. Exponential Smoothing (ETS)
  • B. Autoregressive Integrated Moving Average (AIRMA)
  • C. Prophet
  • D. Convolutional Neural Network - Quantile Regression (CNN-QR)

Answer: D

Explanation:
Explanation
CNN-QR is a proprietary machine learning algorithm for forecasting time series using causal convolutional neural networks (CNNs). CNN-QR works best with large datasets containing hundreds of time series. It accepts item metadata, and is the only Forecast algorithm that accepts related time series data without future values. In this case, the power company has historical power consumption data for the last 10 years, which is a large dataset with multiple time series. The data also includes related data such as weather, number of individuals on the property, and public holidays, which can be used as item metadata or related time series data. Therefore, CNN-QR is the most suitable algorithm for this scenario. References: Amazon Forecast Algorithms, Amazon Forecast CNN-QR


NEW QUESTION # 212
A company processes millions of orders every day. The company uses Amazon DynamoDB tables to store order information. When customers submit new orders, the new orders are immediately added to the DynamoDB tables. New orders arrive in the DynamoDB tables continuously.
A data scientist must build a peak-time prediction solution. The data scientist must also create an Amazon OuickSight dashboard to display near real-lime order insights. The data scientist needs to build a solution that will give QuickSight access to the data as soon as new order information arrives.
Which solution will meet these requirements with the LEAST delay between when a new order is processed and when QuickSight can access the new order information?

  • A. Use Amazon Kinesis Data Firehose to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.
  • B. Use AWS Glue to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.
  • C. Use an API call from OuickSight to access the data that is in Amazon DynamoDB directly
  • D. Use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.

Answer: D

Explanation:
The best solution for this scenario is to use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3, and then configure QuickSight to access the data in Amazon S3. This solution has the following advantages:
It allows near real-time data ingestion from DynamoDB to S3 using Kinesis Data Streams, which can capture and process data continuously and at scale1.
It enables QuickSight to access the data in S3 using the Athena connector, which supports federated queries to multiple data sources, including Kinesis Data Streams2.
It avoids the need to create and manage a Lambda function or a Glue crawler, which are required for the other solutions.
The other solutions have the following drawbacks:
Using AWS Glue to export the data from DynamoDB to S3 introduces additional latency and complexity, as Glue is a batch-oriented service that requires scheduling and configuration3.
Using an API call from QuickSight to access the data in DynamoDB directly is not possible, as QuickSight does not support direct querying of DynamoDB4.
Using Kinesis Data Firehose to export the data from DynamoDB to S3 is less efficient and flexible than using Kinesis Data Streams, as Firehose does not support custom data processing or transformation, and has a minimum buffer interval of 60 seconds5.
References:
1: Amazon Kinesis Data Streams - Amazon Web Services
2: Visualize Amazon DynamoDB insights in Amazon QuickSight using the Amazon Athena DynamoDB connector and AWS Glue | AWS Big Data Blog
3: AWS Glue - Amazon Web Services
4: Visualising your Amazon DynamoDB data with Amazon QuickSight - DEV Community
5: Amazon Kinesis Data Firehose - Amazon Web Services


NEW QUESTION # 213
A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible.
What approach should the Specialist take to accomplish these tasks?

  • A. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph.
  • B. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.
  • C. Run k-means using the Euclidean distance measure for different values of k and create an elbow plot.
  • D. Run k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.

Answer: C


NEW QUESTION # 214
An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.
What combination of services is the MOST efficient to accomplish the task?

  • A. Amazon Transcribe, Amazon Translate, and Amazon Comprehend
  • B. Amazon Transcribe, Amazon Translate, and Amazon SageMaker BlazingText
  • C. Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
  • D. Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq

Answer: A


NEW QUESTION # 215
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