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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q34-Q39):

NEW QUESTION # 34
In the deep neural network (DNN)-hidden Markov model (HMM), the DNN is mainly used for feature processing, while the HMM is mainly used for sequence modeling.

A. TRUE
B. FALSE

Answer: A

Explanation:
In hybridDNN-HMMspeech recognition:
* TheDNNacts as an acoustic model, transforming audio features into probability estimates for phonetic states.
* TheHMMmodels the temporal sequence and transitions between phonetic states, handling time dependencies and variability in speech.
This combination leverages the representational power of DNNs and the sequence modeling strengths of HMMs.
Exact Extract from HCIP-AI EI Developer V2.5:
"In DNN-HMM systems, the DNN outputs state posterior probabilities, and the HMM models the temporal sequence structure of speech." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Hybrid Speech Recognition Models

NEW QUESTION # 35
What type of task is viewed when using the Seq2Seq model in speech recognition?

A. Clustering task
B. Classification task
C. Regression task
D. Dimensionality reduction task

Answer: B

Explanation:
The Seq2Seq (sequence-to-sequence) model converts an input sequence into an output sequence. In speech recognition, the input is a sequence of acoustic features, and the output is a sequence of text tokens. This is essentially aclassification taskbecause each output token is classified into a predefined vocabulary set.
Although the output is sequential, each position in the output sequence involves a classification decision.
Exact Extract from HCIP-AI EI Developer V2.5:
"In speech recognition, Seq2Seq models classify each output token from a fixed vocabulary, making the overall problem a sequence of classification tasks." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Sequence Models in Speech Recognition

NEW QUESTION # 36
John wants to deploy a large model locally to implement the Q&A assistant function for his company. Which of the following factors is unnecessary for John to consider?

A. Output delay
B. Model security
C. Model development framework
D. Demand for computing power

Answer: C

Explanation:
When deploying a pre-trained large model locally for a specific function, themodel development framework used during its creation is generally irrelevant unless modifications or retraining are required. However, John must consider:
* Output delay- to ensure low latency for real-time Q&A.
* Model security- to protect intellectual property and sensitive company data.
* Computing power demand- large models require high-performance hardware.
Exact Extract from HCIP-AI EI Developer V2.5:
"When deploying pre-trained models locally, the deployment plan should address computing resources, performance latency, and security, but does not require re-evaluating the original training framework." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Large Model Deployment Considerations

NEW QUESTION # 37
Which of the following statements about the functions of the encoder and decoder is true?

A. The encoder converts context vectors into variable-length output sequences.
B. The decoder converts variable-length input sequences into fixed-length context vectors, encoding the information of the input sequences in the context vectors.
C. The output lengths of the encoder and decoder are the same.
D. The encoder converts variable-length input sequences into fixed-length context vectors, encoding the information of the input sequences in the context vectors.

Answer: D

Explanation:
In anencoder-decoderarchitecture:
* Theencoderprocesses variable-length inputs and encodes them into fixed-length context vectors that summarize the input. (Cis correct.)
* Thedecodergenerates output sequences from this context, which may be of variable length.
* Adescribes the decoder incorrectly;Bmixes roles;Dis false because output length depends on the target sequence, not the encoder output length.
Exact Extract from HCIP-AI EI Developer V2.5:
"The encoder transforms variable-length sequences into context vectors, which the decoder uses to generate variable-length outputs." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Encoder-Decoder Functions

NEW QUESTION # 38
Which of the following statements about the multi-head attention mechanism of the Transformer are true?

A. Each header's query, key, and value undergo a shared linear transformation to obtain them.
B. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
C. The multi-head attention mechanism captures information about different subspaces within a sequence.
D. The concatenated output is fed directly into the multi-headed attention mechanism.

Answer: B,C

Explanation:
In themulti-head attentionmechanism:
* A:True - the input embedding dimension is split across multiple heads, so each head operates on a lower-dimensional subspace before concatenation.
* B:True - having multiple attention heads allows the model to attend to information from different representation subspaces simultaneously.
* C:False - each head has its own learned linear transformations for queries, keys, and values.
* D:False - after concatenation, the result is passed through a final linear projection, not fed back into the attention module directly.
Exact Extract from HCIP-AI EI Developer V2.5:
"Multi-head attention divides the embedding dimension across heads to learn from multiple subspaces in parallel, then concatenates and linearly projects the result." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Multi-Head Attention

NEW QUESTION # 39
......

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