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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q68-Q73):

NEW QUESTION # 68
A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.
The data scientists are using Amazon Forecast to generate the forecasts.
Which algorithm in Forecast should the data scientists use to meet these requirements?

A. Prophet
B. Exponential Smoothing (ETS)
C. Convolutional Neural Network - Quantile Regression (CNN-QR)
D. Autoregressive Integrated Moving Average (AIRMA)

Answer: C

Explanation:
CNN-QR is a proprietary machine learning algorithm for forecasting time series using causal convolutional neural networks (CNNs). CNN-QR works best with large datasets containing hundreds of time series. It accepts item metadata, and is the only Forecast algorithm that accepts related time series data without future values. In this case, the power company has historical power consumption data for the last 10 years, which is a large dataset with multiple time series. The data also includes related data such as weather, number of individuals on the property, and public holidays, which can be used as item metadata or related time series data. Therefore, CNN-QR is the most suitable algorithm for this scenario. References: Amazon Forecast Algorithms, Amazon Forecast CNN-QR

NEW QUESTION # 69
A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance How should the records be stored in Amazon S3 to improve query performance?

A. CSV files
B. Parquet files
C. RecordIO
D. Compressed JSON

Answer: A

NEW QUESTION # 70
A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test dat a. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.

Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

A. Lower the min_child_weight parameter value.
B. Lower the max_depth parameter value.
C. Update the objective to binary:logistic.
D. Increase the max_depth parameter value.

Answer: B

Explanation:
Overfitting occurs when a model performs well on the training data but poorly on the test data. This is often because the model has learned the training data too well and is not able to generalize to new data. To avoid overfitting, the Machine Learning Specialist should lower the max_depth parameter value. This will reduce the complexity of the model and make it less likely to overfit. According to the XGBoost documentation1, the max_depth parameter controls the maximum depth of a tree and lower values can help prevent overfitting. The documentation also suggests other ways to control overfitting, such as adding randomness, using regularization, and using early stopping1. References:
XGBoost Parameters

NEW QUESTION # 71
A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena The dataset contains more than 800.000 records stored as plaintext CSV files Each record contains 200 columns and is approximately 1 5 MB in size Most queries will span 5 to 10 columns only How should the Machine Learning Specialist transform the dataset to minimize query runtime?

A. Convert the records to XML format
B. Convert the records to JSON format
C. Convert the records to GZIP CSV format
D. Convert the records to Apache Parquet format

Answer: D

Explanation:
Using compressions will reduce the amount of data scanned by Amazon Athena, and also reduce your S3 bucket storage. It's a Win-Win for your AWS bill. Supported formats: GZIP, LZO, SNAPPY (Parquet) and ZLIB.

NEW QUESTION # 72
A company wants to predict the classification of documents that are created from an application. New documents are saved to an Amazon S3 bucket every 3 seconds. The company has developed three versions of a machine learning (ML) model within Amazon SageMaker to classify document text. The company wants to deploy these three versions to predict the classification of each document.
Which approach will meet these requirements with the LEAST operational overhead?

A. Deploy each model to its own SageMaker endpoint. Create three AWS Lambda functions. Configure each Lambda function to call a different endpoint and return the results. Configure three S3 event notifications to invoke the Lambda functions when new documents are created.
B. Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to create three SageMaker batch transform jobs, one batch transform job for each model for each document.
C. Deploy each model to its own SageMaker endpoint Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to call each endpoint and return the results of each model.
D. Deploy all the models to a single SageMaker endpoint. Treat each model as a production variant.
Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to call each production variant and return the results of each model.

Answer: D

Explanation:
The approach that will meet the requirements with the least operational overhead is to deploy all the models to a single SageMaker endpoint, treat each model as a production variant, configure an S3 event notification that invokes an AWS Lambda function when new documents are created, and configure the Lambda function to call each production variant and return the results of each model. This approach involves the following steps:
* Deploy all the models to a single SageMaker endpoint. Amazon SageMaker is a service that can build, train, and deploy machine learning models. Amazon SageMaker can deploy multiple models to a single endpoint, which is a web service that can serve predictions from the models. Each model can be treated as a production variant, which is a version of the model that runs on one or more instances. Amazon SageMaker can distribute the traffic among the production variants according to the specified weights1.
* Treat each model as a production variant. Amazon SageMaker can deploy multiple models to a single endpoint, which is a web service that can serve predictions from the models. Each model can be treated as a production variant, which is a version of the model that runs on one or more instances. Amazon SageMaker can distribute the traffic among the production variants according to the specified weights1.
* Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Amazon S3 is a service that can store and retrieve any amount of data. Amazon S3 can send event notifications when certain actions occur on the objects in a bucket, such as object creation, deletion, or modification. Amazon S3 can invoke an AWS Lambda function as a destination for the event notifications. AWS Lambda is a service that can run code without provisioning or managing servers2.
* Configure the Lambda function to call each production variant and return the results of each model.
AWS Lambda can execute the code that can call the SageMaker endpoint and specify the production variant to invoke. AWS Lambda can use the AWS SDK or the SageMaker Runtime API to send requests to the endpoint and receive the predictions from the models. AWS Lambda can return the results of each model as a response to the event notification3.
The other options are not suitable because:
* Option A: Configuring an S3 event notification that invokes an AWS Lambda function when new documents are created, configuring the Lambda function to create three SageMaker batch transform jobs, one batch transform job for each model for each document, will incur more operational overhead than using a single SageMaker endpoint. Amazon SageMaker batch transform is a service that can process large datasets in batches and store the predictions in Amazon S3. Amazon SageMaker batch transform is not suitable for real-time inference, as it introduces a delay between the request and the response. Moreover, creating three batch transform jobs for each document will increase the complexity and cost of the solution4.
* Option C: Deploying each model to its own SageMaker endpoint, configuring an S3 event notification that invokes an AWS Lambda function when new documents are created, configuring the Lambda function to call each endpoint and return the results of each model, will incur more operational overhead than using a single SageMaker endpoint. Deploying each model to its own endpoint will increase the number of resources and endpoints to manage and monitor. Moreover, calling each endpoint separately will increase the latency and network traffic of the solution5.
* Option D: Deploying each model to its own SageMaker endpoint, creating three AWS Lambda functions, configuring each Lambda function to call a different endpoint and return the results, configuring three S3 event notifications to invoke the Lambda functions when new documents are created, will incur more operational overhead than using a single SageMaker endpoint and a single Lambda function. Deploying each model to its own endpoint will increase the number of resources and endpoints to manage and monitor. Creating three Lambda functions will increase the complexity and cost of the solution. Configuring three S3 event notifications will increase the number of triggers and destinations to manage and monitor6.
References:
* 1: Deploying Multiple Models to a Single Endpoint - Amazon SageMaker
* 2: Configuring Amazon S3 Event Notifications - Amazon Simple Storage Service
* 3: Invoke an Endpoint - Amazon SageMaker
* 4: Get Inferences for an Entire Dataset with Batch Transform - Amazon SageMaker
* 5: Deploy a Model - Amazon SageMaker
* 6: AWS Lambda

NEW QUESTION # 73
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