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AWS Certified Specialty AWS Certified Machine Learning - Specialty

AWS Certified Machine Learning - Specialty

Last Update Jun 16, 2024
Total Questions : 281

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Questions 4

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?

Options:

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.

Discussion 0
Questions 5

A manufacturing company has a production line with sensors that collect hundreds of quality metrics. The company has stored sensor data and manual inspection results in a data lake for several months. To automate quality control, the machine learning team must build an automated mechanism that determines whether the produced goods are good quality, replacement market quality, or scrap quality based on the manual inspection results.

Which modeling approach will deliver the MOST accurate prediction of product quality?

Options:

A.  

Amazon SageMaker DeepAR forecasting algorithm

B.  

Amazon SageMaker XGBoost algorithm

C.  

Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm

D.  

A convolutional neural network (CNN) and ResNet

Discussion 0
Questions 6

A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical

features. The Marketing team has not provided any insight about which features are relevant for churn

prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on

the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide

gap between the training and validation set accuracy.

Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team’s

needs? (Choose two.)

Options:

A.  

Add L1 regularization to the classifier

B.  

Add features to the dataset

C.  

Perform recursive feature elimination

D.  

Perform t-distributed stochastic neighbor embedding (t-SNE)

E.  

Perform linear discriminant analysis

Discussion 0
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Questions 7

A company has an ecommerce website with a product recommendation engine built in TensorFlow. The recommendation engine endpoint is hosted by Amazon SageMaker. Three compute-optimized instances support the expected peak load of the website.

Response times on the product recommendation page are increasing at the beginning of each month. Some users are encountering errors. The website receives the majority of its traffic between 8 AM and 6 PM on weekdays in a single time zone.

Which of the following options are the MOST effective in solving the issue while keeping costs to a minimum? (Choose two.)

Options:

A.  

Configure the endpoint to use Amazon Elastic Inference (EI) accelerators.

B.  

Create a new endpoint configuration with two production variants.

C.  

Configure the endpoint to automatically scale with the Invocations Per Instance metric.

D.  

Deploy a second instance pool to support a blue/green deployment of models.

E.  

Reconfigure the endpoint to use burstable instances.

Discussion 0
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