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AWS Certified Solutions Architect - Associate

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q181-Q186):

NEW QUESTION # 181
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression. During exploratory data analysis, the Specialist observes that many features are highly correlated with each other. This may make the model unstable.
What should be done to reduce the impact of having such a large number of features?

A. Apply the Pearson correlation coefficient.
B. Use matrix multiplication on highly correlated features.
C. Create a new feature space using principal component analysis (PCA)
D. Perform one-hot encoding on highly correlated features.

Answer: C

NEW QUESTION # 182
A company is building a line-counting application for use in a quick-service restaurant. The company wants to use video cameras pointed at the line of customers at a given register to measure how many people are in line and deliver notifications to managers if the line grows too long. The restaurant locations have limited bandwidth for connections to external services and cannot accommodate multiple video streams without impacting other operations.
Which solution should a machine learning specialist implement to meet these requirements?

A. Build a custom model in Amazon SageMaker to recognize the number of people in an image. Install cameras compatible with Amazon Kinesis Video Streams in the restaurant. Write an AWS Lambda function to take an image. Use the SageMaker endpoint to call the model to count people. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.
B. Deploy AWS DeepLens cameras in the restaurant to capture video. Enable Amazon Rekognition on the AWS DeepLens device, and use it to trigger a local AWS Lambda function when a person is recognized. Use the Lambda function to send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.
C. Install cameras compatible with Amazon Kinesis Video Streams to stream the data to AWS over the restaurant's existing internet connection. Write an AWS Lambda function to take an image and send it to Amazon Rekognition to count the number of faces in the image. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.
D. Build a custom model in Amazon SageMaker to recognize the number of people in an image. Deploy AWS DeepLens cameras in the restaurant. Deploy the model to the cameras. Deploy an AWS Lambda function to the cameras to use the model to count people and send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.

Answer: D

Explanation:
The best solution for building a line-counting application for use in a quick-service restaurant is to use the following steps:
Build a custom model in Amazon SageMaker to recognize the number of people in an image. Amazon SageMaker is a fully managed service that provides tools and workflows for building, training, and deploying machine learning models. A custom model can be tailored to the specific use case of line-counting and achieve higher accuracy than a generic model1 Deploy AWS DeepLens cameras in the restaurant to capture video. AWS DeepLens is a wireless video camera that integrates with Amazon SageMaker and AWS Lambda. It can run machine learning inference locally on the device without requiring internet connectivity or streaming video to the cloud. This reduces the bandwidth consumption and latency of the application2 Deploy the model to the cameras. AWS DeepLens allows users to deploy trained models from Amazon SageMaker to the cameras with a few clicks. The cameras can then use the model to process the video frames and count the number of people in each frame2 Deploy an AWS Lambda function to the cameras to use the model to count people and send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long. AWS Lambda is a serverless computing service that lets users run code without provisioning or managing servers. AWS DeepLens supports running Lambda functions on the device to perform actions based on the inference results. Amazon SNS is a service that enables users to send notifications to subscribers via email, SMS, or mobile push23 The other options are incorrect because they either require internet connectivity or streaming video to the cloud, which may impact the bandwidth and performance of the application. For example:
Option A uses Amazon Kinesis Video Streams to stream the data to AWS over the restaurant's existing internet connection. Amazon Kinesis Video Streams is a service that enables users to capture, process, and store video streams for analytics and machine learning. However, this option requires streaming multiple video streams to the cloud, which may consume a lot of bandwidth and cause network congestion. It also requires internet connectivity, which may not be reliable or available in some locations4 Option B uses Amazon Rekognition on the AWS DeepLens device. Amazon Rekognition is a service that provides computer vision capabilities, such as face detection, face recognition, and object detection. However, this option requires calling the Amazon Rekognition API over the internet, which may introduce latency and require bandwidth. It also uses a generic face detection model, which may not be optimized for the line-counting use case.
Option C uses Amazon SageMaker to build a custom model and an Amazon SageMaker endpoint to call the model. Amazon SageMaker endpoints are hosted web services that allow users to perform inference on their models. However, this option requires sending the images to the endpoint over the internet, which may consume bandwidth and introduce latency. It also requires internet connectivity, which may not be reliable or available in some locations.
References:
1: Amazon SageMaker - Machine Learning Service - AWS
2: AWS DeepLens - Deep learning enabled video camera - AWS
3: Amazon Simple Notification Service (SNS) - AWS
4: Amazon Kinesis Video Streams - Amazon Web Services
5: Amazon Rekognition - Video and Image - AWS
6: Deploy a Model - Amazon SageMaker

NEW QUESTION # 183
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 AWS Glue to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.
B. Use an API call from OuickSight to access the data that is in Amazon DynamoDB directly
C. Use Amazon Kinesis Data Firehose to export the data from Amazon DynamoDB to Amazon S3.Configure OuickSight to access the data in Amazon S3.
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 # 184
An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images Which of the following should be used to resolve this issue? (Select TWO)

A. Make the neural network architecture complex.
B. Add vanishing gradient to the model
C. Use gradient checking in the model
D. Add L2 regularization to the model
E. Perform data augmentation on the training data

Answer: D,E

NEW QUESTION # 185
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?

A. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
B. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
C. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.
D. Reduce the learning rate and run the training process until the training loss stops decreasing.

Answer: A

Explanation:
Initializing the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector will provide the maximum performance boost for the LSTM model. Word2vec is a technique that learns distributed representations of words based on their co-occurrence in a large corpus of text. These representations capture semantic and syntactic similarities between words, which can help the LSTM model better understand the meaning and context of the sentences in the text documents. Using word2vec embeddings that are pretrained on a relevant domain (energy sector) can further improve the performance by reducing the vocabulary mismatch and increasing the coverage of the words in the text documents. References
:
* AWS Machine Learning Specialty Exam Guide
* AWS Machine Learning Training - Text Classification with TF-IDF, LSTM, BERT: a comparison of performance
* AWS Machine Learning Training - Machine Learning - Exam Preparation Path

NEW QUESTION # 186
......

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