QUESTION: 61

A Machine Learning Specialist is working with a large cybersecurity 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?

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 S3.
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.
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.
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 data.

Answer(s): A

Reference:

https://aws.amazon.com/tw/blogs/machine-learning/use-the-built-in-amazon-sagemaker-random-cut-forest-algorithm-for-anomaly-detection/

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QUESTION: 62

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?

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

Answer(s): A

Reference:

https://aws.amazon.com/big-data/real-time-analytics-featured-partners/

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QUESTION: 63

A retail company intends to use machine learning to categorize new products. A labeled dataset of current products was provided to the Data Science team. The dataset includes 1,200 products. The labeled dataset has 15 features for each product such as title dimensions, weight, and price. Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.

Which model should be used for categorizing new products using the provided dataset for training?

AnXGBoost model where the objective parameter is set to multi:softmax
A deep convolutional neural network (CNN) with a softmax activation function for the last layer
A regression forest where the number of trees is set equal to the number of product categories
A DeepAR forecasting model based on a recurrent neural network (RNN)

Answer(s): A

Reference:

https://medium.com/@gabrielziegler3/multiclass-multilabel-classification-with-xgboost-66195e4d9f2d

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QUESTION: 64

A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset.

Which tool should be used to improve the validation accuracy?

Amazon Comprehend syntax analysis and entity detection
Amazon SageMaker BlazingText cbow mode
Natural Language Toolkit (NLTK) stemming and stop word removal
Scikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer

Answer(s): D

Reference:

https://monkeylearn.com/sentiment-analysis/

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