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NEW QUESTION: 1
An example of coercive power would be:
A. Disciplinary layoff
B. All of the other alternatives apply
C. Reduction in wages
D. Demotion
E. Reprimand
Answer: B

NEW QUESTION: 2
An application stores payroll information nightly in DynamoDB for a large number of employees across hundreds of offices. Item attributes consist of individual name, office identifier, and cumulative daily hours.
Managers run reports for ranges of names working in their office. One query is. "Return all Items in this office for names starting with A through E".
Which table configuration will result in the lowest impact on provisioned throughput for this query?
A. Configure the table to have a hash index on the name attribute, and a range index on the office identifier
B. Configure the table to have a range index on the name attribute, and a hash index on the office identifier
C. Configure a hash index on the name attribute and no range index
D. Configure a hash index on the office Identifier attribute and no range index
Answer: B
Explanation:
Explanation
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.CoreComponents.html Partition key and sort key - Referred to as a composite primary key, this type of key is composed of two attributes. The first attribute is the partition key, and the second attribute is the sort key. DynamoDB uses the partition key value as input to an internal hash function. The output from the hash function determines the partition (physical storage internal to DynamoDB) in which the item will be stored. All items with the same partition key value are stored together, in sorted order by sort key value.

NEW QUESTION: 3
Which assumption does CA SiteMinder make about a user directory by default?
A. A user will be authorized against the same directory.
B. A user will be audited against the same directory.
C. A user will be authenticated against the same directory.
D. A user will be authenticated AND authorized against the same directory.
Answer: A

NEW QUESTION: 4
A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?
A. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode
B. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode
C. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and tram using the full dataset.
D. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.
Answer: C