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NEW QUESTION: 1
How fast can Cisco Unified Communications Manager generate CDR and CMR files?
A. One CDR file and one CMR file every second for up to 1 minute
B. Ten CDR files and ten CMR files every hour for up to 1 day
C. Ten CDR files and ten CMR files every day for up to 1 week
D. One CDR file and one CMR file every minute for up to 1 hour
Answer: B
NEW QUESTION: 2
While creating a Journal Entry Rule Set, you are not able to use an Account Rule recently created.
Which two options explain that? (Choose two.)
A. The Account Rule is using sources assigned to different event classes from that of the associated Journal Entry Rule Set.
B. The Account Rule's conditions are not defined.
C. The Account Rule's chart of accounts have no account values assigned
D. The Account Rule is defined with a different chart of accounts from the Journal Entry Rule Set.
Answer: A,C
NEW QUESTION: 3
A customer with Avaya Aura Contact Center will use the Contact Router for all calls entering the system. What is one function of the Contact Router?
A. It links Control Directory Numbers (CDN) to applications.
B. It links Intrlnslcs to applications.
C. It links agents to applications.
D. It links skillsets to applications.
Answer: A
NEW QUESTION: 4
An enterprise company's data science team wants to provide a safe, cost-effective way to provide easy access to Amazon SageMaker. The data scientists have limited AWS knowledge and need to be able to launch a Jupyter notebook instance. The notebook instance needs to have a preconfigured AWS KMS key to encrypt data at rest on the machine learning storage volume without exposing the complex setup requirements.
Which approach will allow the company to set up a self-service mechanism for the data scientists to launch Jupyter notebooks in its AWS accounts with the LEAST amount of operational overhead?
A. Create an AWS CLI script that the data scientists can run locally. Provide step-by-step instructions about the parameters to be provided while executing the AWS CLI script to launch a Jupyter notebook with a preconfigured KMS key. Distribute the CLI script to the data scientists using a shared Amazon S3 bucket.
B. Create a serverless front end using a static Amazon S3 website to allow the data scientists to request a Jupyter notebook instance by filling out a form. Use Amazon API Gateway to receive requests from the S3 website and trigger a central AWS Lambda function to make an API call to Amazon SageMaker that launch a notebook instance with a preconfigured KMS key for the data scientists. Then call back to the front-end website to display the URL to the notebook instance.
C. Create an AWS CloudFormation template to launch a Jupyter notebook instance using the AWS::SageMaker::Notebooklnstance resource type with a preconfigured KMS key. Simplify the parameter names, such as the instance size, by mapping them to Small, Large, and X-Large using the Mappings section in CloudFormation. Display the URL to the notebook using the Outputs section, then upload the template into an AWS Service Catalog product in the data scientist's portfolio, and share it with the data scientist's 1AM role.
D. Create an AWS CloudFormation template to launch a Jupyter notebook instance using the AWS::SaqeMaker::Notebooklnstance resource type with a preconfigured KMS key. Add a user-friendly name to the CloudFormation template. Display the URL to the notebook using the Outputs section.
Distribute the CloudFormation template to the data scientists using a shared Amazon S3 bucket.
Answer: D
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