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NEW QUESTION: 1
A company is running a web application with On-Demand Amazon EC2 instances in Auto Scaling groups that scale dynamically based on custom metrics After extensive testing the company determines that the m5
2xlarge instance size is optimal for the workload Application data is stored in db r4 4xlarge Amazon RDS instances that are confirmed to be optimal The traffic to the web application spikes randomly during the day What other cost-optimization methods should the company implement to further reduce costs without impacting the reliability of the application?
A. Reserve capacity for all EC2 instances and leverage Spot Instance pricing for the RDS database
B. Reduce the RDS instance size to db r4 xlarge and add five equivalents sized read replicas to provide reliability
C. Reserve capacity for the RDS database and the minimum number of EC2 instances that are constantly running
D. Double the instance count in the Auto Scaling groups and reduce the instance size to m5 large
Answer: C

NEW QUESTION: 2
Consider the following code:
$_ = "New York";
@array2 = split(//);
What is the value of $array2[0] after the code is executed?
A. "N e w"
B. "N"
C. ""
D. "NewYork"
Answer: B

NEW QUESTION: 3
Your customer wants to capture multiple GBs of aggregate real-time key performance indicators (KPIs) from their game servers running on Google Cloud Platform and monitor the KPIs with low latency. How should they capture the KPIs?
A. Output custom metrics to Stackdriver from the game servers, and create a Dashboard in Stackdriver Monitoring Console to view them.
B. Insert the KPIs into Cloud Datastore entities, and run ad hoc analysis and visualizations of them in Cloud Datalab.
C. Store time-series data from the game servers in Google Bigtable, and view it using Google Data Studio.
D. Schedule BigQuery load jobs to ingest analytics files uploaded to Cloud Storage every ten minutes, and visualize the results in Google Data Studio.
Answer: C
Explanation:
Explanation/Reference:
Reference:https://cloud.google.com/solutions/data-lifecycle-cloud-platform