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NEW QUESTION: 1
HOTSPOT




Answer:
Explanation:

Web tier: Use 2 Standard tier VMs in a new availability set, load balanced with Azure Load Balancer.
The web tier must support high volumes of incoming Internet traffic during peak times throughout the year.
Services: Use 2 Standard Tier VM in a new availability set.
Data: Use 2 Standard tier VMs contained within the services tier availability set.
Processing: Use 2 Dv2-series Vms in a new scale set.
The processing tier must support high volume CPU loads at peak times throughout the year.
Dv2-series, a follow-on to the original D-series, features a more powerful CPU. The Dv2-series CPU is about 35% faster than the D-series CPU.
Automatic scaling of virtual machines in a scale set is the creation or deletion of machines in the set as needed to match performance requirements. As the volume of work grows, an application may require additional resources to enable it to effectively perform tasks.
References:
https://docs.microsoft.com/en-us/azure/virtual-machines/virtual-machines-windows-sizes
https://docs.microsoft.com/en-us/azure/virtual-machine-scale-sets/virtual-machine-scale-sets- autoscale-overview

NEW QUESTION: 2
A security team at an e-commerce company wants to define an automatic incident response process for fraudulent credit card usage attempts. The team targets a 10-minute or faster response time for such incidents. The fraudulent card list is updated every 60 seconds. The e- commerce servers log the transaction details in near-real time. Which option should you recommend to the security team?
A. Maintain a log ingestion exclusion filter based on the fraudulent credit card lists.
B. Use AutoML to automatically build models based on the fraudulent credit card lists.
C. Create a new logging export with a filter to match the transaction and a sink pointing to a Cloud Pub/Sub topic.
D. Define a log-based metric for each fraudulent credit card, and set a Stackdriver alert for these metrics.
Answer: C
Explanation:
A is not correct because creating a metric for every credit card will not scale well.
B is not correct because it will exclude the transactions that are relevant to the security team.
C is not correct because while we could use AutoML to build models, this solution is incomplete without deploying and running the model, as well as wiring them up with some consumer service.
D is correct because this will capture the important events and pass them to Pub/Sub which in turn can send the message to a consumer service like a chat notification webhook.
https://cloud.google.com/logging/docs/export/configure_export_v2

NEW QUESTION: 3
A report author wants to nest a chart within a list and filter the chart data to relate to the row of the list that it is displayed in.
How can this be done?
A. Create a join in the Query explorer to merge the data.
B. Select the list and define a Master detail relationship.
C. Select the chart and define a Master detail relationship.
D. Enable share result set on the list object.
Answer: C

NEW QUESTION: 4
An application writes items to an Amazon DynamoDB table. As the application scales to thousands of instances, calls to the DynamoDB API generate occasional ThrottlingException errors. The application is coded in a language incompatible with the AWS SDK.
How should the error be handled?
A. Add exponential backoff to the application logic
B. Send the items to DynamoDB through Amazon Kinesis Data Firehose
C. Pass API calls through Amazon API Gateway
D. Use Amazon SQS as an API message bus
Answer: A
Explanation:
https://aws.amazon.com/premiumsupport/knowledge-center/throttled-ddb/
SDKs automatically add exponential backoff. If not using the AWS SDKs, add your own backoff logic to the application code. https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Programming.Errors.html#Programming.Errors.Handling