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The Amazon AWS-Certified-Machine-Learning-Specialty exam covers a wide range of topics, including data engineering, data analysis, ML models, AWS services, and deployment and implementation. Candidates are expected to have a strong understanding of ML concepts and techniques, as well as experience working with AWS services such as Amazon SageMaker, Amazon Elastic MapReduce (EMR), and Amazon Simple Storage Service (S3).
The Amazon AWS-Certified-Machine-Learning-Specialty Exam covers a wide range of topics such as data preparation, feature engineering, modeling, evaluation, and deployment. The candidate is expected to have a deep understanding of the AWS infrastructure and services related to machine learning, such as Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, and Amazon Lex, among others.
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NEW QUESTION # 286
A beauty supply store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to generate a report of hourly visitors from the recordings. The report should group visitors by hair style and hair color.
Which solution will meet these requirements with the LEAST amount of effort?
Answer: A
Explanation:
The solution that will meet the requirements with the least amount of effort is to use a semantic segmentation algorithm to identify a visitor's hair in video frames, and pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color. This solution can leverage the existing Amazon SageMaker algorithms and frameworks to perform the tasks of hair segmentation and classification.
Semantic segmentation is a computer vision technique that assigns a class label to every pixel in an image, such that pixels with the same label share certain characteristics. Semantic segmentation can be used to identify and isolate different objects or regions in an image, such as a visitor's hair in a video frame. Amazon SageMaker provides a built-in semantic segmentation algorithm that can train and deploy models for semantic segmentation tasks. The algorithm supports three state-of-the-art network architectures: Fully Convolutional Network (FCN), Pyramid Scene Parsing Network (PSP), and DeepLab v3. The algorithm can also use pre-trained or randomly initialized ResNet-50 or ResNet-101 as the backbone network. The algorithm can be trained using P2/P3 type Amazon EC2 instances in single machine configurations1.
ResNet-50 is a convolutional neural network that is 50 layers deep and can classify images into 1000 object categories. ResNet-50 is trained on more than a million images from the ImageNet database and can achieve high accuracy on various image recognition tasks. ResNet-50 can be used to determine hair style and hair color from the segmented hair regions in the video frames. Amazon SageMaker provides a built-in image classification algorithm that can use ResNet-50 as the network architecture. The algorithm can also perform transfer learning by fine-tuning the pre-trained ResNet-50 model with new data. The algorithm can be trained using P2/P3 type Amazon EC2 instances in single or multiple machine configurations2.
The other options are either less effective or more complex to implement. Using an object detection algorithm to identify a visitor's hair in video frames would not segment the hair at the pixel level, but only draw bounding boxes around the hair regions. This could result in inaccurate or incomplete hair segmentation, especially if the hair is occluded or has irregular shapes. Using an XGBoost algorithm to determine hair style and hair color would require transforming the segmented hair images into numerical features, which could lose some information or introduce noise. XGBoost is also not designed for image classification tasks, and may not achieve high accuracy or performance.
References:
1: Semantic Segmentation Algorithm - Amazon SageMaker
2: Image Classification Algorithm - Amazon SageMaker
NEW QUESTION # 287
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?
Answer: C
Explanation:
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 # 288
A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.
How can the data scientist meet these requirements?
Answer: B
Explanation:
Explanation
The correct solution for updating the software on a SageMaker notebook instance is to stop and then restart the notebook instance. This will automatically apply the latest security and software updates provided by SageMaker1 The other options are incorrect because they either do not update the software or require unnecessary steps.
For example:
Option A calls the CreateNotebookInstanceLifecycleConfig API operation. This operation creates a lifecycle configuration, which is a set of shell scripts that run when a notebook instance is created or started. A lifecycle configuration can be used to customize the notebook instance, such as installing additional libraries or packages. However, it does not update the software on the notebook instance2 Option B creates a new SageMaker notebook instance and mounts the Amazon Elastic Block Store (Amazon EBS) volume from the original instance. This option will create a new notebook instance with the latest software, but it will also incur additional costs and require manual steps to transfer the data and settings from the original instance3 Option D calls the UpdateNotebookInstanceLifecycleConfig API operation. This operation updates an existing lifecycle configuration. As explained in option A, a lifecycle configuration does not update the software on the notebook instance4 References:
1: Amazon SageMaker Notebook Instances - Amazon SageMaker
2: CreateNotebookInstanceLifecycleConfig - Amazon SageMaker
3: Create a Notebook Instance - Amazon SageMaker
4: UpdateNotebookInstanceLifecycleConfig - Amazon SageMaker
NEW QUESTION # 289
An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?
Answer: A
Explanation:
Explanation
Image classification is a machine learning task that involves assigning labels to images based on their content.
Image classification can be performed using various algorithms, such as convolutional neural networks (CNNs), which are a type of deep learning model that can learn to extract high-level features from images. To train a customized image classification model, the e-commerce company needs a compute choice that can support the high computational demands of deep learning and provide good accuracy on the model quickly. A GPU accelerated computing instance, such as p3.2xlarge, is a suitable choice for this task, as it can leverage the parallel processing power of GPUs to speed up the training process and reduce the training time. A p3.2xlarge instance has one NVIDIA Tesla V100 GPU, which can provide up to 125 teraflops of mixed-precision performance and 16 GB of GPU memory. A p3.2xlarge instance can also use various deep learning frameworks, such as TensorFlow, PyTorch, MXNet, etc., to build and train the image classification model. A p3.2xlarge instance is also more cost-effective than a p3.8xlarge instance, which has four NVIDIA Tesla V100 GPUs, as the latter may not be necessary for a proof of concept with a small dataset. Therefore, the Machine Learning Specialist should select p3.2xlarge as the compute choice to train and achieve good accuracy on the model quickly.
References:
Amazon EC2 P3 Instances - Amazon Web Services
Image Classification - Amazon SageMaker
Convolutional Neural Networks - Amazon SageMaker
Deep Learning AMIs - Amazon Web Services
NEW QUESTION # 290
A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults. The model analyzes new loan applications and predicts the risk of loan default. To train the model, the data scientist manually extracted loan data from a database. The data scientist performed the model training and deployment steps in a Jupyter notebook that is hosted on SageMaker Studio notebooks. The model's prediction accuracy is decreasing over time. Which combination of slept in the MOST operationally efficient way for the data scientist to maintain the model's accuracy? (Select TWO.)
Answer: B,D
Explanation:
Option A is correct because SageMaker Pipelines is a service that enables you to create and manage automated workflows for your machine learning projects. You can use SageMaker Pipelines to orchestrate the steps of data extraction, model training, and model deployment in a repeatable and scalable way1.
Option B is correct because SageMaker Model Monitor is a service that monitors the quality of your models in production and alerts you when there are deviations in the model quality. You can use SageMaker Model Monitor to set an accuracy threshold for your model and configure a CloudWatch alarm that triggers when the threshold is exceeded. You can then connect the alarm to the workflow in SageMaker Pipelines to automatically initiate retraining and deployment of a new version of the model2.
Option C is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Creating a daily SageMaker Processing job that reads the predictions from Amazon S3 and checks for changes in model prediction accuracy is a manual and time-consuming process. It also requires you to write custom code to perform the data analysis and send the email notification. Moreover, it does not automatically retrain and deploy the model when the accuracy drops.
Option D is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Rerunning the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model is a manual and error-prone process. It also requires you to monitor the model's performance and initiate the retraining and deployment steps yourself. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.
Option E is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Exporting the training and deployment code from the SageMaker Studio notebooks into a Python script and packaging the script into an Amazon ECS task that an AWS Lambda function can initiate is a complex and cumbersome process. It also requires you to manage the infrastructure and resources for the Amazon ECS task and the AWS Lambda function. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.
References:
1: SageMaker Pipelines - Amazon SageMaker
2: Monitor data and model quality - Amazon SageMaker
NEW QUESTION # 291
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