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Download AWS Certified Machine Learning – Specialty Exam Dumps
NEW QUESTION 23
A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data.
Which solution requires the LEAST effort to be able to query this data?
- A. Use AWS Glue to catalogue the data and Amazon Athena to run queries.
- B. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
- C. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.
- D. Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
NEW QUESTION 24
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 to 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?
- A. Use Amazon Kinesis Data Analytics to stream the input data and perform real-time 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.
- 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.
- C. 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 in Amazon S3 that is accessible for downstream use.
- D. 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 PySpark 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.
Kinesis Data Analytics can not directly stream the input data.
NEW QUESTION 25
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. RecordIO
- B. Parquet files
- C. CSV files
- D. Compressed JSON
NEW QUESTION 26
A company wants to predict the sale prices of houses based on available historical sales dat a. The target variable in the company’s dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model’s complexity?
- A. Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.
- B. Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
- C. Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
- D. Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
NEW QUESTION 27