Online Access Free H13-311_V3.5 Practice Test

Tensorflow is the second generation of artificial intelligence learning system developed by Google based on ( ).

• A.DistBelief
• B.PaleyFunction
• C.Infinity
• D.ConvexOne

On-Device Execution, that is, the entire image is offloaded and executed, and the computing power of the Yiteng chip can be fully utilized, which can greatly reduce the interaction overhead, thereby increasing the accelerator occupancy rate.
On-Device The following description is wrong?

• A.Challenges of model execution under super chip computing power: Memory wall problems, high interaction overhead, and difficulty in data supply. Partly in Host Executed, partly in Device Execution, interaction overhead is even much greater than execution overhead, resulting in low accelerator occupancy
• B.MindSpore Through the chip-oriented depth map optimization technology, the synchronization wait is less, and the "data computing communication" is maximized. The parallelism of "trust", compared with training performance Host Side view scheduling method is flat
• C.MindSpore Realize decentralized autonomy through adaptive graph optimization driven by gradient data A11 Reduce, Gradient aggregation is in step, and calculation and communication are fully streamlined
• D.The challenge of distributed gradient aggregation under super chip computing power:ReslNet50 Single iteration 20ms Time will be generated The synchronization overhead of heart control and the communication overhead of frequent synchronization. Traditional methods require 3 Synchronization completed A11 Reduce, Data-driven method autonomy A11 Reduce, No control overhead

The following are the description of the distribution function. distribution law. and density function of random variables. which is incorrect?

• A.Discrete random variables have no d1slribulion function
• B.Distribution function describes the value rule of random variables
• C.The density function can only describe the value rule of continuous random variables
• D.Distribution law can only describe the value rule of discrete random variables

Reducing the gap between the training error and the test error will result in over-fitting How to prevent over-fitting? (Multiple Choice)

• A.Increase regularization
• B.Cross validation
• C.Feature Engineering
• D.Integration method

Huawei AI The whole scenario includes public cloud, private cloud, various edge computing, IoT industry terminals and consumer. The deployment environment of terminal, edge, and cloud for fee-based terminals.

• A.FALSE
• B.TRUE