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NEW QUESTION 35
A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?

A. AWS Glue with a custom ETL script to transform the data.B. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.C. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.D. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.

Answer: B

 

NEW QUESTION 36
A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested The company also wants be able to save the results in its data lake for later processing and analysis What is the MOST efficient way to accomplish these tasks'?

A. Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.B. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection Then use Kinesis Data Firehose to stream the results to Amazon S3C. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the dataD. Ingest the data into Apache Spark Streaming using Amazon EMR. and use Spark MLlib with k-means to perform anomaly detection Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake

Answer: B

 

NEW QUESTION 37
A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively.
How should the Specialist address this issue and what is the reason behind it?

A. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.B. The learning rate should be increased because the optimization process was trapped at a local minimum.C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.D. The dropout rate at the flatten layer should be increased because the model is not generalized enough.

Answer: A

Explanation:
Explanation/Reference: https://www.tensorflow.org/tutorials/keras/overfit_and_underfit

 

NEW QUESTION 38
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