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Distributed Training in TensorFlow QUIZ (MCQ QUESTIONS AND ANSWERS)

Total Correct: 0

Time:20:00

Question: 1

Which TensorFlow feature enables distributed training across multiple devices or machines?

Question: 2

What is a potential drawback of using model parallelism in distributed TensorFlow training?

Question: 3

Which TensorFlow feature is used for fine-tuning pre-trained models on new datasets?

Question: 4

What is a potential challenge of using data parallelism in distributed TensorFlow training?

Question: 5

Which factor is NOT typically considered when selecting hardware for a distributed TensorFlow setup?

Question: 6

In a distributed TensorFlow setup, what is the primary role of worker nodes?

Question: 7

Which TensorFlow feature enables the efficient distribution of datasets across nodes in distributed training?

Question: 8

What is a key consideration when configuring network communication for distributed TensorFlow training?

Question: 9

Which TensorFlow component facilitates the deployment of trained models for serving predictions?

Question: 10

What is the primary advantage of using gradient accumulation across nodes in distributed training?

Question: 11

Which TensorFlow API is commonly used for implementing data parallelism in distributed training?

Question: 12

What is a potential challenge of using model parallelism in TensorFlow?

Question: 13

Which TensorFlow feature is utilized for efficient communication between nodes during distributed training?

Question: 14

In distributed TensorFlow training, what is the purpose of sharding data?

Question: 15

What is the primary advantage of using data parallelism in distributed TensorFlow setups?

Question: 16

Which TensorFlow feature enables automatic differentiation for computing gradients during training?

Question: 17

What technique can be employed to overcome the memory limitations associated with large model sizes in TensorFlow?

Question: 18

Which type of parallelism in TensorFlow is more suitable for models with large numbers of parameters?

Question: 19

What is a potential drawback of using model parallelism in TensorFlow?

Question: 20

Which TensorFlow component is responsible for managing communication between nodes in a distributed setup?

Question: 21

In a distributed TensorFlow setup, what role does the parameter server typically play?

Question: 22

Which factor is NOT a consideration when setting up a multi-node training system in TensorFlow?

Question: 23

What is the purpose of gradient accumulation across nodes in distributed training?

Question: 24

When might model parallelism be preferred over data parallelism in TensorFlow?

Question: 25

In TensorFlow, what is the primary difference between model parallelism and data parallelism?

Question: 26

What is one advantage of using a multi-node training setup in TensorFlow?

Question: 27

In TensorFlow, what role does the chief worker typically play in a distributed training setup?

Question: 28

What is a primary benefit of using distributed TensorFlow training on multiple GPUs?

Question: 29

Which TensorFlow feature is commonly used for model evaluation and monitoring during training?

Question: 30

What is a potential limitation of using data parallelism in distributed TensorFlow training?