Free Amazon Web Services MLS-C01 Practice Exam with Questions & Answers | Set: 6

 Amazon Kinesis Data Analytics is a service that can analyze streaming data in real time using SQL or Apache Flink applications. It can also use machine learning algorithms, such as Random Cut Forest (RCF), to perform anomaly detection on streaming data. By inserting a Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream, the retail chain can transform the raw record attributes into simple transformed values using SQL queries. This can be done without changing the existing data ingestion process or deploying additional resources. The transformed records can then be outputted to another Kinesis Data Firehose stream that delivers them to Amazon S3 for training the machine learning model. This approach will require the least amount of development effort, as it leverages the existing Kinesis Data Firehose stream and the built-in SQL capabilities of Kinesis Data Analytics.

Amazon Kinesis Data Analytics - Amazon Web Services

Anomaly Detection with Amazon Kinesis Data Analytics - Amazon Web Services

Amazon Kinesis Data Firehose - Amazon Web Services

Amazon S3 - Amazon Web Services

 The solution A and C are the most effective in solving the issue while keeping costs to a minimum. The solution A and C involve the following steps:

Configure the endpoint to use Amazon Elastic Inference (EI) accelerators. This will enable the company to reduce the cost and latency of running TensorFlow inference on SageMaker. Amazon EI provides GPU-powered acceleration for deep learning models without requiring the use of GPU instances. Amazon EI can attach to any SageMaker instance type and provide the right amount of acceleration based on the workload1.

Configure the endpoint to automatically scale with the Invocations Per Instance metric. This will enable the company to adjust the number of instances based on the demand and traffic patterns of the website. The Invocations Per Instance metric measures the average number of requests that each instance processes over a period of time. By using this metric, the company can scale out the endpoint when the load increases and scale in when the load decreases. This can improve the response time and availability of the product recommendation engine2.

The other options are not suitable because:

Option B: Creating a new endpoint configuration with two production variants will not solve the issue of increasing response time and errors. Production variants are used to split the traffic between different models or versions of the same model. They can be useful for testing, updating, or A/B testing models. However, they do not provide any scaling or acceleration benefits for the inference workload3.

Option D: Deploying a second instance pool to support a blue/green deployment of models will not solve the issue of increasing response time and errors. Blue/green deployment is a technique for updating models without downtime or disruption. It involves creating a new endpoint configuration with a different instance pool and model version, and then shifting the traffic from the old endpoint to the new endpoint gradually. However, this technique does not provide any scaling or acceleration benefits for the inference workload4.

Option E: Reconfiguring the endpoint to use burstable instances will not solve the issue of increasing response time and errors. Burstable instances are instances that provide a baseline level of CPU performance with the ability to burst above the baseline when needed. They can be useful for workloads that have moderate CPU utilization and occasional spikes. However, they are not suitable for workloads that have high and consistent CPU utilization, such as the product recommendation engine. Moreover, burstable instances may incur additional charges when they exceed their CPU credits5.

1: Amazon Elastic Inference

2: How to Scale Amazon SageMaker Endpoints

3: Deploying Models to Amazon SageMaker Hosting Services

4: Updating Models in Amazon SageMaker Hosting Services

5: Burstable Performance Instances

 Pushing the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and providing the S3 location within the notebook is the best approach for training a model using the data stored in Amazon RDS. This is because Amazon SageMaker can directly access data from Amazon S3 and train models on it. AWS Data Pipeline is a service that can automate the movement and transformation of data between different AWS services. It can also use Amazon RDS as a data source and Amazon S3 as a data destination. This way, the data can be transferred efficiently and securely without writing any code within the notebook. References:

Amazon SageMaker

AWS Data Pipeline

Data normalization is a data preprocessing technique that scales the features to a common range, such as [0, 1] or [-1, 1]. This helps reduce the impact of features with high magnitude on the cost function and improves the convergence during backpropagation. Data normalization can be done using different methods, such as min-max scaling, z-score standardization, or unit vector normalization. Data normalization is different from dimensionality reduction, which reduces the number of features; model regularization, which adds a penalty term to the cost function to prevent overfitting; and data augmentation, which increases the amount of data by creating synthetic samples. References:

Data processing options for AI/ML | AWS Machine Learning Blog

Data preprocessing - Machine Learning Lens

How to Normalize Data Using scikit-learn in Python

Normalization | Machine Learning | Google for Developers

 The best solution to reduce the cost of the notebook instance and the data preprocessing job is to change the notebook instance type to a smaller general-purpose instance, stop the notebook when it is not in use, and run data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing. This solution will result in the most cost savings because:

Changing the notebook instance type to a smaller general-purpose instance will reduce the hourly cost of running the notebook, since the feature engineering development does not require high CPU and memory resources. For example, an ml.t3.medium instance costs $0.0464 per hour, while an ml.m5.4xlarge instance costs $0.888 per hour1.

Stopping the notebook when it is not in use will also reduce the cost, since the notebook will only incur charges when it is running. For example, if the notebook is used for 8 hours per day, 5 days per week, then stopping it when it is not in use will save about 76% of the monthly cost compared to leaving it running all the time2.

Running data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing will reduce the cost of the data preprocessing job, since the ml.r5 instance is optimized for memory-intensive workloads and has a lower cost per GB of memory than the ml.m5 instance. For example, an ml.r5.4xlarge instance has 128 GB of memory and costs $1.008 per hour, while an ml.m5.4xlarge instance has 64 GB of memory and costs $0.888 per hour1. Therefore, the ml.r5.4xlarge instance can process the same amount of data in half the time and at a lower cost than the ml.m5.4xlarge instance. Moreover, using Amazon SageMaker Processing will allow the data preprocessing job to run on a separate, fully managed infrastructure that can be scaled up or down as needed, without affecting the notebook instance.

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

Option A is not optimal because changing the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has will not reduce the cost of the notebook, since the memory optimized instances have a higher cost per vCPU than the general-purpose instances. For example, an ml.r5.4xlarge instance has 16 vCPUs and costs $1.008 per hour, while an ml.m5.4xlarge instance has 16 vCPUs and costs $0.888 per hour1. Moreover, running both data preprocessing and feature engineering development on the same instance will not take advantage of the scalability and flexibility of Amazon SageMaker Processing.

Option B is not suitable because running data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing will not reduce the cost of the data preprocessing job, since the P3 instance type is optimized for GPU-based workloads and has a higher cost per GB of memory than the ml.m5 or ml.r5 instance types. For example, an ml.p3.2xlarge instance has 61 GB of memory and costs $3.06 per hour, while an ml.m5.4xlarge instance has 64 GB of memory and costs $0.888 per hour1. Moreover, the data preprocessing job does not require GPU, so using a P3 instance type will be wasteful and inefficient.

Option D is not feasible because running data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option will not reduce the cost of the data preprocessing job, since the Reserved Instance option requires a commitment to a consistent amount of usage for a period of 1 or 3 years3. However, the data preprocessing job only runs once a day on average and completes in only 2 hours, so it does not have a consistent or predictable usage pattern. Therefore, using the Reserved Instance option will not provide any cost savings and may incur additional charges for unused capacity.

Amazon SageMaker Pricing

Manage Notebook Instances - Amazon SageMaker

Amazon EC2 Pricing - Reserved Instances

Principal component analysis (PCA) is an unsupervised machine learning algorithm that attempts to reduce the dimensionality (number of features) within a dataset while still retaining as much information as possible. This is done by finding a new set of features called components, which are composites of the original features that are uncorrelated with one another. They are also constrained so that the first component accounts for the largest possible variability in the data, the second component the second most variability, and so on. By using PCA, the impact of having a large number of features that are highly correlated with each other can be reduced, as the new feature space will have fewer dimensions and less redundancy. This can make the linear models more stable and less prone to overfitting. References:

Principal Component Analysis (PCA) Algorithm - Amazon SageMaker

Perform a large-scale principal component analysis faster using Amazon SageMaker | AWS Machine Learning Blog

Machine Learning- Prinicipal Component Analysis | i2tutorials

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 best technique to improve the recall of the MLP for the target class of interest is to add class weights to the MLP’s loss function and then retrain. Class weights are a way of assigning different importance to each class in the dataset, such that the model will pay more attention to the classes with higher weights. This can help mitigate the class imbalance problem, where the model tends to favor the majority class and ignore the minority class. By increasing the weight of the target class of interest, the model will try to reduce the false negatives and increase the true positives, which will improve the recall metric. Adding class weights to the loss function is also a quick and easy solution, as it does not require gathering more data, changing the model architecture, or switching to a different algorithm.

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Deep Learning with Amazon SageMaker

AWS Machine Learning Training - Class Imbalance and Weighted Loss Functions

To read data from an Amazon S3 bucket that is protected with server-side encryption using AWS KMS, the Amazon SageMaker notebook instance needs to have an IAM role that has permission to access the S3 bucket and the KMS key. The IAM role is an identity that defines the permissions for the notebook instance to interact with other AWS services. The IAM role can be assigned to the notebook instance when it is created or updated later.

The KMS key policy is a document that specifies who can use and manage the KMS key. The KMS key policy can grant permission to the IAM role of the notebook instance to decrypt the data in the S3 bucket. The KMS key policy can also grant permission to other principals, such as AWS accounts, IAM users, or IAM roles, to use the KMS key for encryption and decryption operations.

Therefore, the Machine Learning Specialist should assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role. This way, the notebook instance can use the IAM role credentials to access the S3 bucket and the KMS key, and read the encrypted data from the S3 bucket.

Create an IAM Role to Grant Permissions to Your Notebook Instance

Using Key Policies in AWS KMS