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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q329-Q334):

NEW QUESTION # 329
A data scientist stores financial datasets in Amazon S3. The data scientist uses Amazon Athena to query the datasets by using SQL.
The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model. The data scientist wants to obtain inferences from the model at the SageMaker endpoint However, when the data .... ntist attempts to invoke the SageMaker endpoint, the data scientist receives SOL statement failures The data scientist's 1AM user is currently unable to invoke the SageMaker endpoint Which combination of actions will give the data scientist's 1AM user the ability to invoke the SageMaker endpoint? (Select THREE.)

A. Include an inline policy for the data scientist's 1AM user that allows SageMaker to read S3 objects
B. Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemakerGetRecord action.
C. Attach the AmazonAthenaFullAccess AWS managed policy to the user identity.
D. Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemaker: lnvokeEndpoint action,
E. Perform a user remapping in SageMaker to map the 1AM user to another 1AM user that is on the hosted endpoint.
F. Include the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in the Athena SQL query.

Answer: A,D,F

Explanation:
Explanation
The correct combination of actions to enable the data scientist's IAM user to invoke the SageMaker endpoint is B, C, and E, because they ensure that the IAM user has the necessary permissions, access, and syntax to query the ML model from Athena. These actions have the following benefits:
B: Including a policy statement for the IAM user that allows the sagemaker:InvokeEndpoint action grants the IAM user the permission to call the SageMaker Runtime InvokeEndpoint API, which is used to get inferences from the model hosted at the endpoint1.
C: Including an inline policy for the IAM user that allows SageMaker to read S3 objects enables the IAM user to access the data stored in S3, which is the source of the Athena queries2.
E: Including the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in the Athena SQL query allows the IAM user to invoke the ML model as an external function from Athena, which is a feature that enables querying ML models from SQL statements3.
The other options are not correct or necessary, because they have the following drawbacks:
A: Attaching the AmazonAthenaFullAccess AWS managed policy to the user identity is not sufficient, because it does not grant the IAM user the permission to invoke the SageMaker endpoint, which is required to query the ML model4.
D: Including a policy statement for the IAM user that allows the IAM user to perform the sagemaker:GetRecord action is not relevant, because this action is used to retrieve a single record from a feature group, which is not the case in this scenario5.
F: Performing a user remapping in SageMaker to map the IAM user to another IAM user that is on the hosted endpoint is not applicable, because this feature is only available for multi-model endpoints, which are not used in this scenario.
References:
1: InvokeEndpoint - Amazon SageMaker
2: Querying Data in Amazon S3 from Amazon Athena - Amazon Athena
3: Querying machine learning models from Amazon Athena using Amazon SageMaker | AWS Machine Learning Blog
4: AmazonAthenaFullAccess - AWS Identity and Access Management
5: GetRecord - Amazon SageMaker Feature Store Runtime
6: [Invoke a Multi-Model Endpoint - Amazon SageMaker]

NEW QUESTION # 330
A company is running a machine learning prediction service that generates 100 TB of predictions every day. A Machine Learning Specialist must generate a visualization of the daily precision- recall curve from the predictions, and forward a read-only version to the Business team.
Which solution requires the LEAST coding effort?

A. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3. Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team.
B. Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.
C. Run daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3.
Give the Business team read-only access to S3.
D. Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team.

Answer: A

NEW QUESTION # 331
A developer at a retail company is creating a daily demand forecasting model. The company stores the historical hourly demand data in an Amazon S3 bucket. However, the historical data does not include demand data for some hours.
The developer wants to verify that an autoregressive integrated moving average (ARIMA) approach will be a suitable model for the use case.
How should the developer verify the suitability of an ARIMA approach?

A. Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Impute missing hourly values. Choose ARIMA as the machine learning (ML) problem. Check the model performance.
B. Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Choose ARIMA as the machine learning (ML) problem. Check the model performance.
C. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Resample data by using the aggregate daily total. Perform a Seasonal Trend decomposition.
D. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Impute hourly missing data.
Perform a Seasonal Trend decomposition.

Answer: D

Explanation:
The best solution to verify the suitability of an ARIMA approach is to use Amazon SageMaker Data Wrangler. Data Wrangler is a feature of SageMaker Studio that provides an end-to-end solution for importing, preparing, transforming, featurizing, and analyzing data. Data Wrangler includes built-in analyses that help generate visualizations and data insights in a few clicks. One of the built-in analyses is the Seasonal-Trend decomposition, which can be used to decompose a time series into its trend, seasonal, and residual components. This analysis can help the developer understand the patterns and characteristics of the time series, such as stationarity, seasonality, and autocorrelation, which are important for choosing an appropriate ARIMA model. Data Wrangler also provides built-in transformations that can help the developer handle missing data, such as imputing with mean, median, mode, or constant values, or dropping rows with missing values. Imputing missing data can help avoid gaps and irregularities in the time series, which can affect the ARIMA model performance. Data Wrangler also allows the developer to export the prepared data and the analysis code to various destinations, such as SageMaker Processing, SageMaker Pipelines, or SageMaker Feature Store, for further processing and modeling.
The other options are not suitable for verifying the suitability of an ARIMA approach. Amazon SageMaker Autopilot is a feature-set that automates key tasks of an automatic machine learning (AutoML) process. It explores the data, selects the algorithms relevant to the problem type, and prepares the data to facilitate model training and tuning. However, Autopilot does not support ARIMA as a machine learning problem type, and it does not provide any visualization or analysis of the time series data. Resampling data by using the aggregate daily total can reduce the granularity and resolution of the time series, which can affect the ARIMA model accuracy and applicability.

NEW QUESTION # 332
A data scientist is using an Amazon SageMaker notebook instance and needs to securely access data stored in a specific Amazon S3 bucket.
How should the data scientist accomplish this?

A. Encrypt the objects in the S3 bucket with a custom AWS Key Management Service (AWS KMS) key that only the notebook owner has access to.
B. Add an S3 bucket policy allowing GetObject, PutObject, and ListBucket permissions to the Amazon SageMaker notebook ARN as principal.
C. Attach the policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket.
D. Use a script in a lifecycle configuration to configure the AWS CLI on the instance with an access key ID and secret.

Answer: C

Explanation:
Explanation
The best way to securely access data stored in a specific Amazon S3 bucket from an Amazon SageMaker notebook instance is to attach a policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket. This way, the notebook can use the AWS SDK or CLI to access the S3 bucket without exposing any credentials or requiring any additional configuration.
This is also the recommended approach by AWS for granting access to S3 from SageMaker. References:
Amazon SageMaker Roles
Accessing Amazon S3 from a SageMaker Notebook Instance

NEW QUESTION # 333
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 dat a. All these transformations must finish running in 30-60 minutes.
Which solution will meet these requirements MOST cost-effectively?

A. 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.
B. 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.
C. 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.
D. 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.

Answer: A

Explanation:
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.
References:
Using Amazon S3 Event Notifications
Prepare ML Data with Amazon SageMaker Data Wrangler
AWS Lambda Function

NEW QUESTION # 334
......

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