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What Is Audience for AWS Machine Learning Specialty Certification?

The AWS Certified Machine Learning Specialty certificate is intended for programmers, data scientists, and other candidates passionate about machine learning who want to learn how to use the benefits of artificial intelligence capabilities on the AWS platform. The training process necessary for obtaining this certification helps examinees develop the right skills to build, train, and deploy machine learning models using advanced AWS Cloud services. Candidates can achieve this certificate by obtaining the passing score in MLS-C01 exam. Even though this test doesn't have any mandatory requirements, the vendor recommends that candidates should have previous knowledge of certain topics. A successful applicant is one who has between 1 to 2 years of practical experience in developing, running and architecting ML and deep learning workloads on the AWS Cloud. Also, it would be helpful if the candidate would have prior experience performing basic hyperparameter optimization and know how to follow model-training and operational best practices.

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To earn the Amazon MLS-C01 certification, candidates must pass a rigorous exam that covers a wide range of topics related to machine learning on AWS. These topics include data preparation and feature engineering, model selection and training, tuning and optimization, deployment and operationalization, and security and compliance. AWS-Certified-Machine-Learning-Specialty Exam consists of 65 multiple-choice and multiple-response questions that must be completed within 180 minutes.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q57-Q62):

NEW QUESTION # 57
An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time.
Which solution should the agency consider?

• A. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected.
• B. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection of known employees, and alert when non-employees are detected.
• C. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detect faces from a collection of known employees and alert when non-employees are detected.
• D. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when nonemployees are detected.

Answer: B

Explanation:
The solution that the agency should consider is to use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection of known employees, and alert when non-employees are detected.
This solution has the following advantages:
It can handle thousands of video cameras in real time, as Amazon Kinesis Video Streams can scale elastically to support any number of producers and consumers1.
It can leverage the Amazon Rekognition Video API, which is designed and optimized for video analysis, and can detect faces in challenging conditions such as low lighting, occlusions, and different poses2.
It can use a stream processor, which is a feature of Amazon Rekognition Video that allows you to create a persistent application that analyzes streaming video and stores the results in a Kinesis data stream3. The stream processor can compare the detected faces with a collection of known employees, which is a container for persisting faces that you want to search for in the input video stream4. The stream processor can also send notifications to Amazon Simple Notification Service (Amazon SNS) when non-employees are detected, which can trigger downstream actions such as sending alerts or storing the events in Amazon Elasticsearch Service (Amazon ES)3.
References:
1: What Is Amazon Kinesis Video Streams? - Amazon Kinesis Video Streams
2: Detecting and Analyzing Faces - Amazon Rekognition
3: Using Amazon Rekognition Video Stream Processor - Amazon Rekognition
4: Working with Stored Faces - Amazon Rekognition

 

NEW QUESTION # 58
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric.
This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours.
With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s).
Which visualization will accomplish this?

• A. A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.
• B. A scatter plot showing the correlation between maximum tree depth and the objective metric.
• C. A scatter plot showing the performance of the objective metric over each training iteration.
• D. A histogram showing whether the most important input feature is Gaussian.

Answer: A

Explanation:
https://medium.com/all-things-ai/in-depth-parameter-tuning-for-random-forest-d67bb7e920d

 

NEW QUESTION # 59
A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker.
Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?

• A. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster.
• B. Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3.
• C. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.
• D. Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3.

Answer: C

Explanation:
The solution that will accomplish the necessary transformation to train the Amazon SageMaker model with the least amount of administrative overhead is to crawl the data using AWS Glue crawlers, write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3. This solution leverages the serverless capabilities of AWS Glue to automatically discover the schema of the data sources, and to perform the data integration and transformation without requiring any cluster management or configuration. The output in CSV format is compatible with Amazon SageMaker and can be easily loaded into a training job. References: AWS Glue, Amazon SageMaker

 

NEW QUESTION # 60
The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data.
Which machine learning algorithm should the researchers use that BEST meets their requirements?

• A. Latent Dirichlet Allocation (LDA)
• B. Convolutional neural network (CNN)
• C. Recurrent neural network (RNN)
• D. K-means

Answer: B

Explanation:
The problem of detecting whether or not individuals in a collection of images are wearing the company's retail brand is an example of image recognition, which is a type of machine learning task that identifies and classifies objects in an image. Convolutional neural networks (CNNs) are a type of machine learning algorithm that are well-suited for image recognition, as they can learn to extract features from images and handle variations in size, shape, color, and orientation of the objects. CNNs consist of multiple layers that perform convolution, pooling, and activation operations on the input images, resulting in a high-level representation that can be used for classification or detection. Therefore, option D is the best choice for the machine learning algorithm that meets the requirements of the chief editor.
Option A is incorrect because latent Dirichlet allocation (LDA) is a type of machine learning algorithm that is used for topic modeling, which is a task that discovers the hidden themes or topics in a collection of text documents. LDA is not suitable for image recognition, as it does not preserve the spatial information of the pixels. Option B is incorrect because recurrent neural networks (RNNs) are a type of machine learning algorithm that are used for sequential data, such as text, speech, or time series. RNNs can learn from the temporal dependencies and patterns in the input data, and generate outputs that depend on the previous states.
RNNs are not suitable for image recognition, as they do not capture the spatial dependencies and patterns in the input images. Option C is incorrect because k-means is a type of machine learning algorithm that is used for clustering, which is a task that groups similar data points together based on their features. K-means is not suitable for image recognition, as it does not perform classification or detection of the objects in the images.
References:
* Image Recognition Software - ML Image & Video Analysis - Amazon ...
* Image classification and object detection using Amazon Rekognition ...
* AWS Amazon Rekognition - Deep Learning Face and Image Recognition ...
* GitHub - awslabs/aws-ai-solution-kit: Machine Learning APIs for common ...
* Meet iNaturalist, an AWS-powered nature app that helps you identify ...

 

NEW QUESTION # 61
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

• A. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
• B. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
• C. The validation channel identifying the location of validation data on an Amazon S3 bucket.
• D. Hyperparameters in a JSON array as documented for the algorithm used.
• E. The output path specifying where on an Amazon S3 bucket the trained model will persist.
• F. The training channel identifying the location of training data on an Amazon S3 bucket.

Answer: A,B,E

 

NEW QUESTION # 62
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