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NEW QUESTION 21
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 Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.
• B. Use AWS Glue to catalogue the data and Amazon Athena to run queries.
• C. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
• D. Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.

Answer: B

NEW QUESTION 22
A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago Which method should the Specialist try to improve model performance?

• A. The model needs to be completely re-engineered because it is unable to handle product inventory changes
• B. The model's hyperparameters should be periodically updated to prevent drift
• C. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
• D. The model should be periodically retrained using the original training data plus new data as product inventory changes

Answer: A

NEW QUESTION 23
A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3 The source systems send data in CSV format in real lime The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3 Which solution takes the LEAST effort to implement?

• A. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
• B. Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet
• C. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.
• D. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.

Answer: A

Explanation:
https://medium.com/searce/convert-csv-json-files-to-apache-parquet-using-aws-glue-a760d177b45f
https://github.com/ecloudvalley/Building-a-Data-Lake-with-AWS-Glue-and-Amazon-S3

NEW QUESTION 24
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