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NEW QUESTION: 1
The following SAS program is submitted:

The current system date macro variable is 30JUL2013.
Which output will be written to the log by the program?
A. In two years it will be: 2014+1+1
B. In two years it will be: 2014+1
0
C. In two years it will be: 2013+1+1+1
D. Error: Required operator not found in expression
in two years it will be: 2013+12+1
Answer: A

NEW QUESTION: 2
Server R defines a global queue named Q1. R is also the owner of Q1. Servers P and S define routed queues Q1@R. The servers P, R, and S are connected by routes. Which two statements are true? (Choose two.)
A. When a message is sent from server S to Q1 and the route is not connected, the message is not delivered, and the client application receives the exception DESTINATION_NOT_AVAILABLE.
B. When a message is sent from server P to queue Q1, P forwards the message to the home queue on server R.
C. Messages are available to local and remote consumers even before the message reaches the home queue.
D. If Server P or the route connection from P to R fails, a consumer application of server P will not be able to receive messages from Q1 until the servers resume communication.
Answer: B,D

NEW QUESTION: 3
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?
A. 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.
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 S3.
C. 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.
D. 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: B
Explanation:
https://aws.amazon.com/tw/blogs/machine-learning/use-the-built-in-amazon-sagemaker-random- cut-forest-algorithm-for-anomaly-detection/