AWS-Certified-Machine-Learning-Specialty Free Practice Test

A Data Scientist needs to migrate an existing on-premises ETL process to the cloud The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing
The Data Scientist has been given the following requirements for the cloud solution
* Combine multiple data sources
* Reuse existing PySpark logic
* Run the solution on the existing schedule
* Minimize the number of servers that will need to be managed
Which architecture should the Data Scientist use to build this solution?

1. A. Write the raw data to Amazon S3 Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule Use the existing PySpark logic to run the ETL job on the EMR cluster Output the results to a "processed" location m Amazon S3 that is accessible tor downstream use
2. B. Write the raw data to Amazon S3 Create an AWS Glue ETL job to perform the ETL processing against the input data Write the ETL job in PySpark to leverage the existing logic Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule Configure the output target of the ETL job to write to a "processed" location in Amazon S3 that is accessible for downstream use.
3. C. Write the raw data to Amazon S3 Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3 Write the Lambda logic in Python and implement the existing PySpartc logic to perform the ETL process Have the Lambda function output the results to a "processed" location in Amazon S3 that is accessible for downstream use
4. D. Use Amazon Kinesis Data Analytics to stream the input data and perform realtime SQL queries against the stream to carry out the required transformations within the stream Deliver the output results to a "processed" location in Amazon S3 that is accessible for downstream use

Correct Answer: A

A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs The workflow consists of the following processes
* Start the workflow as soon as data is uploaded to Amazon S3
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3
* Store the results of joining datasets in Amazon S3
* If one of the jobs fails, send a notification to the Administrator Which configuration will meet these requirements?

1. A. Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
2. B. Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
3. C. Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3 Use AWS Glue to join the datasets in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
4. D. Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure

Correct Answer: A

A logistics company needs a forecast model to predict next month's inventory requirements for a single item in 10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.
Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)

1. A. Set PerformAutoML to true.
2. B. Set ForecastHorizon to 4.
3. C. Set ForecastFrequency to W for weekly.
4. D. Set PerformHPO to true.
5. E. Set FeaturizationMethodName to filling.

Correct Answer: CD

A Machine Learning Specialist is training a model to identify the make and model of vehicles in images The Specialist wants to use transfer learning and an existing model trained on images of general objects The Specialist collated a large custom dataset of pictures containing different vehicle makes and models

1. A. Initialize the model with random weights in all layers including the last fully connected layer
2. B. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
3. C. Initialize the model with random weights in all layers and replace the last fully connected layer
4. D. Initialize the model with pre-trained weights in all layers including the last fully connected layer

Correct Answer: D

A Data Engineer needs to build a model using a dataset containing customer credit card information. How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

1. A. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VP
2. B. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
3. C. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
4. D. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VP
5. E. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
6. F. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

Correct Answer: D