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NEW QUESTION: 1
DRAG DROP


Answer:
Explanation:

NEW QUESTION: 2
A media advertising company handles a large number of real-time messages sourced from over
200 websites in real time. Processing latency must be kept low. Based on calculations, a 60- shard Amazon Kinesis stream is more than sufficient to handle the maximum data throughput, even with traffic spikes. The company also uses an Amazon Kinesis Client Library (KCL) application running on Amazon Elastic Compute Cloud (EC2) managed by an Auto Scaling group. Amazon CloudWatch indicates an average of 25% CPU and a modest level of network traffic across all running servers.
The company reports a 150% to 200% increase in latency of processing messages from Amazon Kinesis during peak times. There are NO reports of delay from the sites publishing to Amazon Kinesis.
What is the appropriate solution to address the latency?
A. Increase the size of the Amazon EC2 instances to increase network throughput.
B. Increase the number of shards in the Amazon Kinesis stream to 80 for greater concurrency.
C. Increase the minimum number of instances in the Auto Scaling group.
D. Increase Amazon DynamoDB throughput on the checkpoint table.
Answer: D

NEW QUESTION: 3
Your team is building a data engineering and data science development environment.
The environment must support the following requirements:
* support Python and Scala
* compose data storage, movement, and processing services into automated data pipelines
* the same tool should be used for the orchestration of both data engineering and data science
* support workload isolation and interactive workloads
* enable scaling across a cluster of machines
You need to create the environment.
What should you do?
A. Build the environment in Azure Databricks and use Azure Container Instances for orchestration.
B. Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.
C. Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.
D. Build the environment in Azure Databricks and use Azure Data Factory for orchestration.
Answer: D
Explanation:
In Azure Databricks, we can create two different types of clusters.
* Standard, these are the default clusters and can be used with Python, R, Scala and SQL
* High-concurrency
Azure Databricks is fully integrated with Azure Data Factory.
Incorrect Answers:
D: Azure Container Instances is good for development or testing. Not suitable for production workloads.
References:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machinelearning

NEW QUESTION: 4
Which of the following is NOT possible with a Transformer Stage?
A. Use system variables and job parameters within derivations
B. Generate multiple output records for each single record
C. Define variables that can be referenced only within the Transformer Stage
D. Create and compile a custom function to be used in output link derivations
Answer: D