| Question.71 A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset Which tool should be used to improve the validation accuracy? (A) Amazon Comprehend syntax analysts and entity detection (B) Amazon SageMaker BlazingText allow mode (C) Natural Language Toolkit (NLTK) stemming and stop word removal (D) Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers |
71. Click here to View Answer
Correct Answer: D
Term frequency-inverse document frequency (TF-IDF) is a technique that assigns a weight to each word in a document based on how important it is to the meaning of the document. The term frequency (TF) measures how often a word appears in a document, while the inverse document frequency (IDF) measures how rare a word is across a collection of documents. The TF-IDF weight is the product of the TF and IDF values, and it is high for words that are frequent in a specific document but rare in the overall corpus. TF-IDF can help improve the validation accuracy of a sentiment analysis model by reducing the impact of common words that have little or no sentiment value, such as “the”, “a”, “and”, etc. Scikit-learn is a popular Python library for machine learning that provides a TF-IDF vectorizer class that can transform a collection of text documents into a matrix of TF-IDF features. By using this tool, the Data Scientist can create a more informative and discriminative feature representation for the sentiment analysis task.
TfidfVectorizer – scikit-learn
Text feature extraction – scikit-learn
TF-IDF for Beginners | by Jana Schmidt | Towards Data Science
Sentiment Analysis: Concept, Analysis and Applications | by Susan Li | Towards Data Science
| Question.72 A machine learning (ML) engineer has created a feature repository in Amazon SageMaker Feature Store for the company. The company has AWS accounts for development, integration, and production. The company hosts a feature store in the development account. The company uses Amazon S3 buckets to store feature values offline. The company wants to share features and to allow the integration account and the production account to reuse the features that are in the feature repository. Which combination of steps will meet these requirements? (Select TWO.) (A) 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. (B) Share the feature repository that is associated the S3 buckets from the development account to the integration account and the production account by using AWS Resource Access Manager (AWS RAM). (C) Use AWS Security Token Service (AWS STS) from the integration account and the production account to retrieve credentials for the development account. (D) Set up S3 replication between the development S3 buckets and the integration and production S3 buckets. (E) Create an AWS PrivateLink endpoint in the development account for SageMaker. |
72. Click here to View Answer
Correct Answer: A,B
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
| Question.73 A company needs to develop a model that uses a machine learning (ML) model for risk analysis. An ML engineer needs to evaluate the contribution each feature of a training dataset makes to the prediction of the target variable before the ML engineer selects features. How should the ML engineer predict the contribution of each feature? (A) Use the Amazon SageMaker Data Wrangler multicollinearity measurement features and the principal component analysis (PCA) algorithm to calculate the variance of the dataset along multiple directions in the feature space. (B) Use an Amazon SageMaker Data Wrangler quick model visualization to find feature importance scores that are between 0.5 and 1. (C) Use the Amazon SageMaker Data Wrangler bias report to identify potential biases in the data related to feature engineering. (D) Use an Amazon SageMaker Data Wrangler data flow to create and modify a data preparation pipeline.Manually add the feature scores. |
73. Click here to View Answer
Correct Answer: B
To evaluate the contribution of each feature before model training, feature importance scores are a primary tool. In Amazon SageMaker Data Wrangler, a Quick Model can be used to rapidly generate a model on the dataset and return feature importance scores. These scores indicate how much each feature influences the target variable.
“Use Quick Model to generate feature importance scores quickly to help understand which features contribute the most to predictions. These scores range from 0 to 1, with higher values indicating higher contribution.” This allows ML engineers to assess feature relevance without full-scale model development, thus saving time and effort in the feature selection phase of the pipeline.
| Question.74 A retail company stores 100 GB of daily transactional data in Amazon S3 at periodic intervals. The company wants to identify the schema of the transactional data. The company also wants to perform transformations on the transactional data that is in Amazon S3. The company wants to use a machine learning (ML) approach to detect fraud in the transformed data. Which combination of solutions will meet these requirements with the LEAST operational overhead? {Select THREE.) (A) Use Amazon Athena to scan the data and identify the schema. (B) Use AWS Glue crawlers to scan the data and identify the schema. (C) Use Amazon Redshift to store procedures to perform data transformations (D) Use AWS Glue workflows and AWS Glue jobs to perform data transformations. (E) Use Amazon Redshift ML to train a model to detect fraud. (F) Use Amazon Fraud Detector to train a model to detect fraud. |
74. Click here to View Answer
Correct Answer: B,D,F
To meet the requirements with the least operational overhead, the company should use AWS Glue crawlers, AWS Glue workflows and jobs, and Amazon Fraud Detector. AWS Glue crawlers can scan the data in Amazon S3 and identify the schema, which is then stored in the AWS Glue Data Catalog. AWS Glue workflows and jobs can perform data transformations on the data in Amazon S3 using serverless Spark or Python scripts. Amazon Fraud Detector can train a model to detect fraud using the transformed data and the company’s historical fraud labels, and then generate fraud predictions using a simple API call.
Option A is incorrect because Amazon Athena is a serverless query service that can analyze data in Amazon S3 using standard SQL, but it does not perform data transformations or fraud detection.
Option C is incorrect because Amazon Redshift is a cloud data warehouse that can store and query data using SQL, but it requires provisioning and managing clusters, which adds operational overhead. Moreover, Amazon Redshift does not provide a built-in fraud detection capability.
Option E is incorrect because Amazon Redshift ML is a feature that allows users to create, train, and deploy machine learning models using SQL commands in Amazon Redshift. However, using Amazon Redshift ML would require loading the data from Amazon S3 to Amazon Redshift, which adds complexity and cost. Also, Amazon Redshift ML does not support fraud detection as a use case.
AWS Glue Crawlers
AWS Glue Workflows and Jobs
Amazon Fraud Detector
| Question.75 A data engineer is preparing a dataset that a retail company will use to predict the number of visitors to stores. The data engineer created an Amazon S3 bucket. The engineer subscribed the S3 bucket to an AWS Data Exchange data product for general economic indicators. The data engineer wants to join the economic indicator data to an existing table in Amazon Athena to merge with the business data. All these transformations must finish running in 30-60 minutes. Which solution will meet these requirements MOST cost-effectively? (A) Configure the AWS Data Exchange product as a producer for an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery stream to transfer the data to Amazon S3 Run an AWS Glue job that will merge the existing business data with the Athena table. Write the result set back to Amazon S3. (B) Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to use Amazon SageMaker Data Wrangler to merge the existing business data with the Athena table. Write the result set back to Amazon S3. (C) Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda Function Program the Lambda function to run an AWS Glue job that will merge the existing business data with the Athena table Write the results back to Amazon S3. (D) Provision an Amazon Redshift cluster. Subscribe to the AWS Data Exchange product and use the product to create an Amazon Redshift Table Merge the data in Amazon Redshift. Write the results back to Amazon S3. |
75. Click here to View Answer
Correct Answer: B
The most cost-effective solution is to use an S3 event to trigger a Lambda function that uses SageMaker Data Wrangler to merge the data. This solution avoids the need to provision and manage any additional resources, such as Kinesis streams, Firehose delivery streams, Glue jobs, or Redshift clusters. SageMaker Data Wrangler provides a visual interface to import, prepare, transform, and analyze data from various sources, including AWS Data Exchange products. It can also export the data preparation workflow to a Python script that can be executed by a Lambda function. This solution can meet the time requirement of 30-60 minutes, depending on the size and complexity of the data.
Using Amazon S3 Event Notifications
Prepare ML Data with Amazon SageMaker Data Wrangler
AWS Lambda Function