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NEW QUESTION: 1
You have a Hyper-V host that runs Windows Server 2016.
You need to identify the amount of processor resources consumed by Hyper-V and virtual machines.
Which counter should you use from Performance Monitor?
A. \Hyper-V Hypervisor\Logical Processors
B. \Hyper-V Hypervisor Virtual Processor(_Total)\% Hypervisor Run Time
C. \Hyper-V Hypervisor Logical Processor(_Total)\% Total Run Time
D. \Hyper-V Hypervisor Root Virtual Processor(_Total)\% Guest Run Time
Answer: C
NEW QUESTION: 2
A Security Engineer at a medium-sized accounting firm has been tasked with discovering how much information can be obtained from the firm's public facing web servers. The engineer decides to start by using netcat to port 80.
The engineer receives this output:
Which of the following is an example of what the engineer performed?
A. Whois database query
B. Cross-site scripting
C. SQL injection
D. Banner grabbing
Answer: D
NEW QUESTION: 3
View the Exhibit and examine the description for the CUSTOMERS table.
You want to update the CUST_INCOME_LEVEL and CUST_CREDIT_LIMIT columns for the customer with the CUST_ID 2360. You want the value for the CUST_INCOME_LEVEL to have the same value as that of the customer with the CUST_ID 2560 and the CUST_CREDIT_LIMIT to have the same value as that of the customer with CUST_ID 2566.
Which UPDATE statement will accomplish the task?
A. UPDATE customers SET cust_income_level = (SELECT cust_income_level FROM customers WHERE cust_id = 2560), cust_credit_limit = (SELECT cust_credit_limit FROM customers WHERE cust_id = 2566) WHERE cust_id=2360;
B. UPDATE customers SET (cust_income_level, cust_credit_limit) = (SELECT cust_income_level, cust_credit_limit FROM customers WHERE cust_id=2560 AND cust_id=2566) WHERE cust_id=2360;
C. UPDATE customers SET (cust_income_level, cust_credit_limit) = (SELECT cust_income_level, cust_credit_limit FROM customers WHERE cust_id IN(2560, 2566) WHERE cust_id=2360;
D. UPDATE customers SET (cust_income_level, cust_credit_limit) = (SELECT
cust_income_level, cust_credit_limit
FROM customers
WHERE cust_id=2560 OR cust_id=2566)
WHERE cust_id=2360;
Answer: A
Explanation:
Updating Two Columns with a Subquery
You can update multiple columns in the SET clause of an UPDATE statement by writing multiple
subqueries. The syntax is as follows:
UPDATE table
SET column =
(SELECT column
FROM table
WHERE condition)
[ ,
column =
(SELECT column
FROM table
WHERE condition)]
[WHERE condition ] ;
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 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.
B. 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.
C. 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.
D. 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 IAM role.
Answer: D
Explanation:
https://aws.amazon.com/blogs/mt/enable-self-service-secured-data-science-using-amazon-sagemaker-notebooks-and-aws-service-catalog/
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