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

The best option is to use Amazon SageMaker Studio to rebuild the model and deploy it at an endpoint. Then, use Amazon SageMaker Model Monitor to store inferences and use the inferences to create Shapley values that help explain model behavior. Shapley values are a way of attributing the contribution of each feature to the model output. They can help the credit team understand why the model makes certain decisions and how the features affect the model outcomes. A chart that shows features and SHapley Additive exPlanations (SHAP) values can be created using the SHAP library in Python. This option is the most operationally efficient because it leverages the existing XGBoost training container and the built-in capabilities of Amazon SageMaker Model Monitor and SHAP library. References:

Amazon SageMaker Studio

Amazon SageMaker Model Monitor

SHAP library

The best way to fix the problem without changing the Lambda code or data in DynamoDB is to add the unrecognized words as synonyms in the custom slot type. This way, Amazon Lex can resolve the synonyms to the corresponding slot values and pass them to the Lambda function. For example, if the slot type has a value “comedy” with synonyms “funny”, “fun”, and “humor”, then any of these words entered by the user will be resolved to “comedy” and the Lambda function can query the DynamoDB table for the book titles in that category. Adding synonyms to the custom slot type can be done easily using the Amazon Lex console or API, and does not require any code changes.

The other options are not correct because:

Option A: Adding the unrecognized words in the enumeration values list as new values in the slot type would not fix the problem, because the Lambda function and the DynamoDB table are not aware of these new values. The Lambda function would not be able to query the DynamoDB table for the book titles in the new categories, and the request would still fail. Moreover, adding new values to the slot type would increase the complexity and maintenance of the chatbot, as the Lambda function and the DynamoDB table would have to be updated accordingly.

Option B: Creating a new custom slot type, adding the unrecognized words to this slot type as enumeration values, and using this slot type for the slot would also not fix the problem, for the same reasons as option A. The Lambda function and the DynamoDB table would not be able to handle the new slot type and its values, and the request would still fail. Furthermore, creating a new slot type would require more effort and time than adding synonyms to the existing slot type.

Option C: Using the AMAZON.SearchQuery built-in slot types for custom searches in the database is not a suitable approach for this use case. The AMAZON.SearchQuery slot type is used to capture free-form user input that corresponds to a search query. However, this slot type does not perform any validation or resolution of the user input, and passes the raw input to the Lambda function. This means that the Lambda function would have to handle the logic of parsing and matching the user input to the DynamoDB table, which would require changing the Lambda code and adding more complexity to the solution.

Custom slot type - Amazon Lex

Using Synonyms - Amazon Lex

Built-in Slot Types - Amazon Lex

AWS Glue jobs is the AWS service or feature that will meet the requirements with the least development effort over time. AWS Glue jobs is a fully managed service that enables data engineers to run Apache Spark applications on a serverless Spark environment. AWS Glue jobs can perform batch preprocessing data transformations on large datasets stored in Amazon S3, such as converting data formats, filtering data, joining data, and aggregating data. AWS Glue jobs can also scale the Spark environment automatically based on the data volume and processing needs, without requiring any infrastructure provisioning or management. AWS Glue jobs can reduce the development effort and time by providing a graphical interface to create and monitor Spark applications, as well as a code generation feature that can generate Scala or Python code based on the data sources and targets. AWS Glue jobs can also integrate with other AWS services, such as Amazon Athena, Amazon EMR, and Amazon SageMaker, to enable further data analysis and machine learning tasks1.

The other options are either more complex or less scalable than AWS Glue jobs. Amazon EMR cluster is a managed service that enables data engineers to run Apache Spark applications on a cluster of Amazon EC2 instances. However, Amazon EMR cluster requires more development effort and time than AWS Glue jobs, as it involves setting up, configuring, and managing the cluster, as well as writing and deploying the Spark code. Amazon EMR cluster also does not scale automatically, but requires manual or scheduled resizing of the cluster based on the data volume and processing needs2. Amazon Athena is a serverless interactive query service that enables data engineers to analyze data stored in Amazon S3 using standard SQL. However, Amazon Athena is not suitable for performing complex data transformations, such as joining data from multiple sources, aggregating data, or applying custom logic. Amazon Athena is also not designed for running Spark applications, but only supports SQL queries3. AWS Lambda is a serverless compute service that enables data engineers to run code without provisioning or managing servers. However, AWS Lambda is not optimized for running Spark applications, as it has limitations on the execution time, memory size, and concurrency of the functions. AWS Lambda is also not integrated with Amazon S3, and requires additional steps to read and write data from S3 buckets.

1: AWS Glue - Fully Managed ETL Service - Amazon Web Services

2: Amazon EMR - Amazon Web Services

3: Amazon Athena – Interactive SQL Queries for Data in Amazon S3

[4]: AWS Lambda – Serverless Compute - Amazon Web Services

Amazon Comprehend is a natural language processing (NLP) service that can perform part-of-speech tagging and key phrase extraction tasks. AWS Deep Learning Containers are Docker images that are pre-installed with popular deep learning frameworks such as Apache MXNet. Amazon SageMaker is a fully managed service that can help build, train, and deploy machine learning models. Using Amazon Comprehend for the text preprocessing tasks and AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier is the solution that can be built most quickly to meet the requirements.

Amazon Comprehend

AWS Deep Learning Containers

Amazon SageMaker

 Based on the model evaluation results, this is a viable model for production because the model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives. The accuracy of the model is the proportion of correct predictions out of the total predictions, which can be calculated by adding the true positives and true negatives and dividing by the total number of observations. In this case, the accuracy of the model is (10 + 76) / 100 = 0.86, which means that the model correctly predicted 86% of the customers’ churn status. The cost incurred by the company as a result of false positives and false negatives is the loss or damage that the company suffers when the model makes incorrect predictions. A false positive is when the model predicts that a customer will churn, but the customer actually does not churn. A false negative is when the model predicts that a customer will not churn, but the customer actually churns. In this case, the cost of a false positive is the incentive that the company offers to the customer who is predicted to churn, which is a relatively low cost. The cost of a false negative is the revenue that the company loses when the customer churns, which is a relatively high cost. Therefore, the cost of a false positive is less than the cost of a false negative, and the company would prefer to have more false positives than false negatives. The model has 10 false positives and 4 false negatives, which means that the company’s cost is lower than if the model had more false negatives and fewer false positives.

The best solution for encrypting images at rest and in transit, and opting out of data usage for service improvement, is to use the following steps:

Enable server-side encryption on the S3 bucket. This will encrypt the images stored in the bucket using AWS Key Management Service (AWS KMS) customer master keys (CMKs). This will protect the data at rest from unauthorized access1

Submit an AWS Support ticket to opt out of allowing images to be used for improving the service, and follow the process provided by AWS Support. This will prevent AWS from storing or using the images processed by Amazon Rekognition for service development or enhancement purposes. This will protect the data privacy and ownership2

Use HTTPS to call the Amazon Rekognition CompareFaces API operation. This will encrypt the data in transit between the client and the server using SSL/TLS protocols. This will protect the data from interception or tampering3

The other options are incorrect because they either do not encrypt the images at rest or in transit, or do not opt out of data usage for service improvement. For example:

Option B switches to using an Amazon Rekognition collection to store the images. A collection is a container for storing face vectors that are calculated by Amazon Rekognition. It does not encrypt the images at rest or in transit, and it does not opt out of data usage for service improvement. It also requires changing the API operations from CompareFaces to IndexFaces and SearchFacesByImage, which may not have the same functionality or performance4

Option C switches to using the AWS GovCloud (US) Region for Amazon S3 and Amazon Rekognition. The AWS GovCloud (US) Region is an isolated AWS Region designed to host sensitive data and regulated workloads in the cloud. It does not automatically encrypt the images at rest or in transit, and it does not opt out of data usage for service improvement. It also requires migrating the data and the application to a different Region, which may incur additional costs and complexity5

Option D enables client-side encryption on the S3 bucket. This means that the client is responsible for encrypting and decrypting the images before uploading or downloading them from the bucket. This adds extra overhead and complexity to the client application, and it does not encrypt the data in transit when calling the Amazon Rekognition API. It also does not opt out of data usage for service improvement.

1: Protecting Data Using Server-Side Encryption with AWS KMS–Managed Keys (SSE-KMS) - Amazon Simple Storage Service

2: Opting Out of Content Storage and Use for Service Improvements - Amazon Rekognition

3: HTTPS - Wikipedia

4: Working with Stored Faces - Amazon Rekognition

5: AWS GovCloud (US) - Amazon Web Services

Protecting Data Using Client-Side Encryption - Amazon Simple Storage Service

Amazon Redshift ML enables in-database machine learning model creation and predictions, allowing data scientists to leverage Redshift for model training without needing to export data.

To create and run a model for customer churn prediction in Amazon Redshift ML:

Define the feature variables and target variable: Identify the columns to use as features (predictors) and the target variable (outcome) for the churn prediction model.

Create the model: Write a CREATE MODEL SQL statement, which trains the model using Amazon Redshift’s integration with Amazon SageMaker and stores the model directly in Redshift.

Run predictions: Use the SQL PREDICT function to generate predictions on new data directly within Redshift.

Options B, D, and E are not required as Redshift ML handles model creation and prediction without manual data export to Amazon S3 or additional Spectrum integration.

B. Apply principal component analysis (PCA): PCA is a technique that reduces the dimensionality of a dataset by transforming the original features into a smaller set of new features that capture most of the variance in the data. PCA can help address the issue of multicollinearity, which occurs when some features are highly correlated with each other and can cause problems for some machine learning algorithms. By applying PCA, the data engineer can reduce the number of features and remove the redundancy in the data.

C. Remove a portion of highly correlated features from the dataset: Another way to deal with multicollinearity is to manually remove some of the features that are highly correlated with each other. This can help simplify the model and avoid overfitting. The data engineer can use the correlation matrix to identify the features that have a high correlation coefficient (e.g., above 0.8 or below -0.8) and remove one of them from the dataset. References: =

Principal Component Analysis: This is a document from AWS that explains what PCA is, how it works, and how to use it with Amazon SageMaker.

Multicollinearity: This is a document from AWS that describes what multicollinearity is, how to detect it, and how to deal with it.

The combination of steps that will meet the requirements are to create an IAM role in the development account that the integration account and production account can assume, attach IAM policies to the role that allow access to the feature repository and the S3 buckets, and share the feature repository that is associated with the S3 buckets from the development account to the integration account and the production account by using AWS Resource Access Manager (AWS RAM). This approach will enable cross-account access and sharing of the features stored in Amazon SageMaker Feature Store and Amazon S3.

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share curated data used in training and prediction workflows. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. A feature group schema consists of a list of feature definitions, each of which specifies the name, type, and metadata of a feature. Amazon SageMaker Feature Store stores the features in both an online store and an offline store. The online store is a low-latency, high-throughput store that is optimized for real-time inference. The offline store is a historical store that is backed by an Amazon S3 bucket and is optimized for batch processing and model training1.

AWS Identity and Access Management (IAM) is a web service that helps you securely control access to AWS resources for your users. You use IAM to control who can use your AWS resources (authentication) and what resources they can use and in what ways (authorization). An IAM role is an IAM identity that you can create in your account that has specific permissions. You can use an IAM role to delegate access to users, applications, or services that don’t normally have access to your AWS resources. For example, you can create an IAM role in your development account that allows the integration account and the production account to assume the role and access the resources in the development account. You can attach IAM policies to the role that specify the permissions for the feature repository and the S3 buckets. You can also use IAM conditions to restrict the access based on the source account, IP address, or other factors2.

AWS Resource Access Manager (AWS RAM) is a service that enables you to easily and securely share AWS resources with any AWS account or within your AWS Organization. You can share AWS resources that you own with other accounts using resource shares. A resource share is an entity that defines the resources that you want to share, and the principals that you want to share with. For example, you can share the feature repository that is associated with the S3 buckets from the development account to the integration account and the production account by creating a resource share in AWS RAM. You can specify the feature group ARN and the S3 bucket ARN as the resources, and the integration account ID and the production account ID as the principals. You can also use IAM policies to further control the access to the shared resources3.

The other options are either incorrect or unnecessary. Using AWS Security Token Service (AWS STS) from the integration account and the production account to retrieve credentials for the development account is not required, as the IAM role in the development account can provide temporary security credentials for the cross-account access. Setting up S3 replication between the development S3 buckets and the integration and production S3 buckets would introduce redundancy and inconsistency, as the S3 buckets are already shared through AWS RAM. Creating an AWS PrivateLink endpoint in the development account for SageMaker is not relevant, as it is used to securely connect to SageMaker services from a VPC, not from another account.

1: Amazon SageMaker Feature Store – Amazon Web Services

2: What Is IAM? - AWS Identity and Access Management

3: What Is AWS Resource Access Manager? - AWS Resource Access Manager

 The best strategy to identify fraudulent accounts is to create a FindMatches machine learning transform in AWS Glue. The FindMatches transform enables you to identify duplicate or matching records in your dataset, even when the records do not have a common unique identifier and no fields match exactly. This can help you improve fraud detection by finding accounts that are associated with a previously known fraudulent user. You can teach the FindMatches transform your definition of a “duplicate” or a “match” through examples, and it will use machine learning to identify other potential duplicates or matches in your dataset. You can then use the FindMatches transform in your AWS Glue ETL jobs to cleanse your data.

Option A is incorrect because there is no built-in FindDuplicates Amazon Athena query. Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. However, Amazon Athena does not provide a predefined query to find duplicate records in a dataset. You would have to write your own SQL query to perform this task, which might not be as effective or accurate as using the FindMatches transform.

Option C is incorrect because creating an AWS Glue crawler to infer duplicate accounts in the source data is not a valid strategy. An AWS Glue crawler is a program that connects to a data store, progresses through a prioritized list of classifiers to determine the schema for your data, and then creates metadata tables in the AWS Glue Data Catalog. A crawler does not perform any data cleansing or record matching tasks.

Option D is incorrect because searching for duplicate accounts in the AWS Glue Data Catalog is not a feasible strategy. The AWS Glue Data Catalog is a central repository to store structural and operational metadata for your data assets. The Data Catalog does not store the actual data, but rather the metadata that describes where the data is located, how it is formatted, and what it contains. Therefore, you cannot search for duplicate records in the Data Catalog.

Record matching with AWS Lake Formation FindMatches - AWS Glue

Amazon Athena – Interactive SQL Queries for Data in Amazon S3

AWS Glue Crawlers - AWS Glue

AWS Glue Data Catalog - AWS Glue