MLS-C01 Exam Dumps - Amazon Web Services AWS Certified Specialty Questions and Answers

The best solution to meet the requirements of the company is to use AWS DataSync to make an initial copy of the entire dataset, and schedule subsequent incremental transfers of changing data until the final cutover from on premises to AWS. This is because:

AWS DataSync is an online data movement and discovery service that simplifies data migration and helps you quickly, easily, and securely transfer your file or object data to, from, and between AWS storage services 1. AWS DataSync can copy data between on-premises object storage and Amazon S3, and also supports encryption, scheduling, monitoring, and data integrity validation 1.

AWS DataSync can make an initial copy of the entire dataset by using a DataSync agent, which is a software appliance that connects to your on-premises storage and manages the data transfer to AWS 2. The DataSync agent can be deployed as a virtual machine (VM) on your existing hypervisor, or as an Amazon EC2 instance in your AWS account 2.

AWS DataSync can schedule subsequent incremental transfers of changing data by using a task, which is a configuration that specifies the source and destination locations, the options for the transfer, and the schedule for the transfer 3. You can create a task to run once or on a recurring schedule, and you can also use filters to include or exclude specific files or objects based on their names or prefixes 3.

AWS DataSync can perform the final cutover from on premises to AWS by using a sync task, which is a type of task that synchronizes the data in the source and destination locations 4. A sync task transfers only the data that has changed or that doesn’t exist in the destination, and also deletes any files or objects from the destination that were deleted from the source since the last sync 4.

Therefore, by using AWS DataSync, the company can create a data repository in the AWS Cloud for machine learning projects, and use Amazon S3 for the data storage, while meeting the requirements of encryption, scheduling, monitoring, and data integrity validation.

Data Transfer Service - AWS DataSync

Deploying a DataSync Agent

Creating a Task

Syncing Data with AWS DataSync

The solution C will meet the requirements with the least development effort because it uses Amazon Rekognition and AWS CloudTrail, which are fully managed services that can provide the desired functionality. The solution C involves the following steps:

Use Amazon Rekognition to identify celebrities in the pictures. Amazon Rekognition is a service that can analyze images and videos and extract insights such as faces, objects, scenes, emotions, and more. Amazon Rekognition also provides a feature called Celebrity Recognition, which can recognize thousands of celebrities across a number of categories, such as politics, sports, entertainment, and media. Amazon Rekognition can return the name, face, and confidence score of the recognized celebrities, as well as additional information such as URLs and biographies1.

Use AWS CloudTrail to capture IP address and timestamp details. AWS CloudTrail is a service that can record the API calls and events made by or on behalf of AWS accounts. AWS CloudTrail can provide information such as the source IP address, the user identity, the request parameters, and the response elements of the API calls. AWS CloudTrail can also deliver the event records to an Amazon S3 bucket or an Amazon CloudWatch Logs group for further analysis and auditing2.

The other options are not suitable because:

Option A: Using AWS Panorama to identify celebrities in the pictures and using AWS CloudTrail to capture IP address and timestamp details will not meet the requirements effectively. AWS Panorama is a service that can extend computer vision to the edge, where it can run inference on video streams from cameras and other devices. AWS Panorama is not designed for identifying celebrities in pictures, and it may not provide accurate or relevant results. Moreover, AWS Panorama requires the use of an AWS Panorama Appliance or a compatible device, which may incur additional costs and complexity3.

Option B: Using AWS Panorama to identify celebrities in the pictures and making calls to the AWS Panorama Device SDK to capture IP address and timestamp details will not meet the requirements effectively, for the same reasons as option A. Additionally, making calls to the AWS Panorama Device SDK will require more development effort than using AWS CloudTrail, as it will involve writing custom code and handling errors and exceptions4.

Option D: Using Amazon Rekognition to identify celebrities in the pictures and using the text detection feature to capture IP address and timestamp details will not meet the requirements effectively. The text detection feature of Amazon Rekognition is used to detect and recognize text in images and videos, such as street names, captions, product names, and license plates. It is not suitable for capturing IP address and timestamp details, as these are not part of the pictures that users upload. Moreover, the text detection feature may not be accurate or reliable, as it depends on the quality and clarity of the text in the images and videos5.

1: Amazon Rekognition Celebrity Recognition

2: AWS CloudTrail Overview

3: AWS Panorama Overview

4: AWS Panorama Device SDK

5: Amazon Rekognition Text Detection

When submitting Amazon SageMaker training jobs using one of the built-in algorithms, the common parameters that must be specified are:

The training channel identifying the location of training data on an Amazon S3 bucket. This parameter tells SageMaker where to find the input data for the algorithm and what format it is in. For example, TrainingInputMode: File means that the input data is in files stored in S3.

The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users. This parameter grants SageMaker the necessary permissions to access the S3 buckets, ECR repositories, and other AWS resources needed for the training job. For example, RoleArn: arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-20200303T150948 means that SageMaker will use the specified role to run the training job.

The output path specifying where on an Amazon S3 bucket the trained model will persist. This parameter tells SageMaker where to save the model artifacts, such as the model weights and parameters, after the training job is completed. For example, OutputDataConfig: {S3OutputPath: s3://my-bucket/my-training-job} means that SageMaker will store the model artifacts in the specified S3 location.

The validation channel identifying the location of validation data on an Amazon S3 bucket is an optional parameter that can be used to provide a separate dataset for evaluating the model performance during the training process. This parameter is not required for all algorithms and can be omitted if the validation data is not available or not needed.

The hyperparameters in a JSON array as documented for the algorithm used is another optional parameter that can be used to customize the behavior and performance of the algorithm. This parameter is specific to each algorithm and can be used to tune the model accuracy, speed, complexity, and other aspects. For example, HyperParameters: {num_round: "10", objective: "binary:logistic"} means that the XGBoost algorithm will use 10 boosting rounds and the logistic loss function for binary classification.

The Amazon EC2 instance class specifying whether training will be run using CPU or GPU is not a parameter that is specified when submitting a training job using a built-in algorithm. Instead, this parameter is specified when creating a training instance, which is a containerized environment that runs the training code and algorithm. For example, ResourceConfig: {InstanceType: ml.m5.xlarge, InstanceCount: 1, VolumeSizeInGB: 10} means that SageMaker will use one m5.xlarge instance with 10 GB of storage for the training instance.

Train a Model with Amazon SageMaker

Use Amazon SageMaker Built-in Algorithms or Pre-trained Models

CreateTrainingJob - Amazon SageMaker Service

The input mode for the training job determines how the training data is transferred from Amazon S3 to the SageMaker instance. There are two input modes: File and Pipe. File mode copies the entire training dataset from S3 to the local file system of the instance before starting the training job. This can cause a long delay before the training job launches, especially if the dataset is large. Pipe mode streams the data from S3 to the instance as the training job runs. This can reduce the startup time and improve the I/O throughput, as the data is read in smaller batches. Therefore, to address the performance issues with the current solution, the Specialist should ensure that the input mode for the training job is set to Pipe. This can be done by using the SageMaker Python SDK and setting the input_mode parameter to Pipe when creating the estimator or the fit method12. Alternatively, this can be done by using the AWS CLI and setting the InputMode parameter to Pipe when creating the training job3.

Access Training Data - Amazon SageMaker

Choosing Data Input Mode Using the SageMaker Python SDK - Amazon SageMaker

CreateTrainingJob - Amazon SageMaker Service

The solution C is the most likely to improve the data ingestion rate into Amazon S3 because it increases the number of shards for the data stream. The number of shards determines the throughput capacity of the data stream, which affects the rate of data ingestion. Each shard can support up to 1 MB per second of data input and 2 MB per second of data output. By increasing the number of shards, the company can increase the data ingestion rate proportionally. The company can use the UpdateShardCount API operation to modify the number of shards in the data stream1.

The other options are not likely to improve the data ingestion rate into Amazon S3 because:

Option A: Increasing the number of S3 prefixes for the delivery stream to write to will not affect the data ingestion rate, as it only changes the way the data is organized in the S3 bucket. The number of S3 prefixes can help to optimize the performance of downstream applications that read the data from S3, but it does not impact the performance of Kinesis Data Firehose2.

Option B: Decreasing the retention period for the data stream will not affect the data ingestion rate, as it only changes the amount of time the data is stored in the data stream. The retention period can help to manage the data availability and durability, but it does not impact the throughput capacity of the data stream3.

Option D: Adding more consumers using the Kinesis Client Library (KCL) will not affect the data ingestion rate, as it only changes the way the data is processed by downstream applications. The consumers can help to scale the data processing and handle failures, but they do not impact the data ingestion into S3 by Kinesis Data Firehose4.

1: Resharding - Amazon Kinesis Data Streams

2: Amazon S3 Prefixes - Amazon Kinesis Data Firehose

3: Data Retention - Amazon Kinesis Data Streams

4: Developing Consumers Using the Kinesis Client Library - Amazon Kinesis Data Streams

AWS Glue is a serverless data integration service that can catalogue, clean, enrich, and move data between various data stores. Amazon Athena is an interactive query service that can run SQL queries on data stored in Amazon S3. By using AWS Glue to catalogue the data and Amazon Athena to run queries, the Machine Learning Specialist can leverage the existing data in Amazon S3 without any additional data transformation or loading. This solution requires the least effort compared to the other options, which involve more complex and costly data processing and storage services. References: AWS Glue, Amazon Athena

The solution C meets the requirements because it minimizes costs for compute infrastructure, maximizes the scalability of resources for training, and facilitates the use of an ML model in low-connectivity environments. The solution C involves the following steps:

Move the training data to an Amazon S3 bucket. This will enable the company to store the large amount of data in a durable, scalable, and cost-effective way. It will also allow the company to access the data from the cloud for training and evaluation purposes1.

Train and evaluate the model by using Amazon SageMaker. This will enable the company to use a fully managed service that provides various features and tools for building, training, tuning, and deploying ML models. Amazon SageMaker can handle large-scale data processing and distributed training, and it can leverage the power of AWS compute resources such as Amazon EC2, Amazon EKS, and AWS Fargate2.

Optimize the model by using SageMaker Neo. This will enable the company to reduce the size of the model and improve its performance and efficiency. SageMaker Neo can compile the model into an executable that can run on various hardware platforms, such as CPUs, GPUs, and edge devices3.

Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. This will enable the company to deploy the model on a local device that can run inference in real time, even in low-connectivity environments. AWS IoT Greengrass can extend AWS cloud capabilities to the edge, and it can securely communicate with the cloud for updates and synchronization4.

Deploy the model on the edge device. This will enable the company to automate quality control in its facilities by using the model to detect defects in new parts as they move on a conveyor belt. The model can run inference locally on the edge device without requiring internet connectivity, and it can send the results to the cloud when the connection is available4.

The other options are not suitable because:

Option A: Deploying the model on a SageMaker hosting services endpoint will not facilitate the use of the model in low-connectivity environments, as it will require internet access to perform inference. Moreover, it may incur higher costs for hosting and data transfer than deploying the model on an edge device.

Option B: Training and evaluating the model on premises will not minimize costs for compute infrastructure, as it will require the company to maintain and upgrade its own hardware and software. Moreover, it will not maximize the scalability of resources for training, as it will limit the company’s ability to leverage the cloud’s elasticity and flexibility.

Option D: Training the model on premises will not minimize costs for compute infrastructure, nor maximize the scalability of resources for training, for the same reasons as option B.

1: Amazon S3

2: Amazon SageMaker

3: SageMaker Neo

4: AWS IoT Greengrass

Option C is correct because augmenting the images in the dataset can help the model learn more features and generalize better to new products. Image augmentation is a common technique to increase the diversity and size of the training data.

Option E is correct because Amazon Rekognition Custom Labels can train a custom model to detect specific objects and scenes that are relevant to the business use case. It can also leverage the existing models from Amazon Rekognition that are trained on tens of millions of images across many categories.

Option F is correct because class imbalance can affect the performance and accuracy of the model, as it can cause the model to be biased towards the majority class and ignore the minority class. Applying oversampling or undersampling can help balance the classes and improve the model’s ability to learn from the data.

Option A is incorrect because the semantic segmentation algorithm is used to assign a label to every pixel in an image, not to classify the whole image into a category. Semantic segmentation is useful for applications such as autonomous driving, medical imaging, and satellite imagery analysis.

Option B is incorrect because the DetectLabels API is a general-purpose image analysis service that can detect objects, scenes, and concepts in an image, but it cannot be customized to the specific product lines of the ecommerce company. The DetectLabels API is based on the pre-trained models from Amazon Rekognition, which may not cover all the categories that the company needs.

Option D is incorrect because normalizing the pixels and scaling the images are preprocessing steps that should be done before training the model, not after. These steps can help improve the model’s convergence and performance, but they are not sufficient to increase the accuracy of the model on new products.

 Image Augmentation - Amazon SageMaker

 Amazon Rekognition Custom Labels Features

[Handling Imbalanced Datasets in Machine Learning]

[Semantic Segmentation - Amazon SageMaker]

[DetectLabels - Amazon Rekognition]

[Image Classification - MXNet - Amazon SageMaker]

[https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]

[https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]

[https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]

[https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

[https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]

[https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]

[https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]

[https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

[https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]

[https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]

[https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]

[https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

 The best approach to maximize transcription accuracy during the development phase is to create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. A custom vocabulary is a list of words and phrases that are likely to appear in your audio input, along with optional information about how to pronounce them. By using a custom vocabulary, you can improve the transcription accuracy of domain-specific terms, such as product names, that may not be recognized by the general vocabulary of Amazon Transcribe. You can also analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.

The other options are not as effective as option C for the following reasons:

Option A is not suitable because Amazon Lex is a service for building conversational interfaces, not for transcribing voicemail messages. Amazon Lex also has a limit of 100 slots per bot, which is not enough to accommodate the 200 unique product names required by the company.

Option B is not optimal because it relies on the word confidence scores in the transcript, which may not be accurate enough to identify all the mis-transcribed product names. Moreover, automatically creating or updating a custom vocabulary file may introduce errors or inconsistencies in the pronunciation or display of the words.

Option D is not feasible because it requires a large amount of training data to build a custom language model. The company only has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts, which is not enough to train a robust and reliable custom language model. Additionally, creating and updating a custom language model is a time-consuming and resource-intensive process, which may not be suitable for the development phase where frequent changes are expected.

Amazon Transcribe – Custom Vocabulary

Amazon Transcribe – Custom Language Models

[Amazon Lex – Limits]

Amazon Fraud Detector is a managed service designed to detect potentially fraudulent online activities, such as transactions. It uses machine learning and business rules to classify activities as fraudulent or legitimate, minimizing the need for custom model training. By using the Amazon Fraud Detector prediction API, the company can automatically approve or reject transactions flagged as fraudulent, implementing an efficient risk mitigation strategy without extensive operational effort.

This approach requires minimal setup and effectively allows the company to block fraudulent transactions with high confidence, addressing the business's need to balance risk mitigation and customer impact.