Exam DP-100 topic 1 question 34 discussion

Actual exam question from Microsoft's DP-100

Question #: 34
Topic #: 1

This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You have been tasked with constructing a machine learning model that translates language text into a different language text.
The machine learning model must be constructed and trained to learn the sequence of the.
Recommendation: You make use of Recurrent Neural Networks (RNNs).
Will the requirements be satisfied?

A. Yes
B. No

Show Suggested Answer

Suggested Answer: A 🗳️
Note: RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both. They're called recurrent because the network's hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory.
It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.
Reference:
https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571

by Gabonia at Aug. 19, 2022, 2:18 p.m.

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james2033

7 months, 4 weeks ago

Selected Answer: A

'Machine translation' in sequence way, use Recurrent Neural Networks (RNN). If not the request did not contain 'sequence' way, use Transformer is contemporary best practise.

upvoted 1 times

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dispoc

8 months, 4 weeks ago

Selected Answer: A

RNN are good for learning of sequence

upvoted 1 times

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noname_123

1 year, 3 months ago

https://learn.microsoft.com/en-us/azure/machine-learning/concept-deep-learning-vs-machine-learning Recurrent neural network (RNN) Recurrent neural networks are a widely used artificial neural network. These networks save the output of a layer and feed it back to the input layer to help predict the layer's outcome. Recurrent neural networks have great learning abilities. They're widely used for complex tasks such as time series forecasting, learning handwriting, and recognizing language.

upvoted 2 times

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Gabonia

1 year, 9 months ago

Correct

upvoted 4 times

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