CRITICAL CARE HESI PRACTICE ACTUAL TEST
PAPER 2026 COMPLETE QUESTIONS AND
CORRECT ANSWERS GRADED A+
▶ What are the layers used in Transformer to capture time dependencies
within a sequence? (Multiple choice)
A. Residual layer
B. Self-attention layer
C. Location code
D. Feed-forward network layer - Answer: BC
▶ Which of the following hyperparameters may be defined during model
training? (Multiple choice)
A. Batch size
B. Learning rate
C. Optimizer type
D. Loss value - Answer: ABC
▶ Which of the following are common evaluation metrics for classification
tasks? (Multiple choice)
A. Accuracy
B. Precision
C. Recall rate
D. Mean squared error - Answer: ABC
▶ Which of the following statements about data preprocessing are true?
(Multiple choice)
A. Data cleansing is a process of filling in missing values, as well as
detecting and eliminating noise and exceptions.
B. Data dimension reduction aims to simplify data attributes and avoid the
curse of dimensionality.
C. Data standardization aims to reduce noise data and improve model
accuracy by standardizing data.
D. Machine learning tasks generate results in the form of model outputs.
Therefore, model training is more important than data preprocessing. -
Answer: ABC
, ▶ Which of the following statements about model parameters and
hyperparameters are true? (Multiple choice)
A. Models contain both parameters and hyperparameters.
B. Hyperparameters are automatically learned by models.
C. Hyperparameters are manually set.
D. Hyperparameters can be used to control training. - Answer: ACD
▶ Data augmentation can improve model robustness and avoid overfitting.
Which of the following are data augmentation methods? (Multiple choice)
A. Batch normalization
B. Image contrast change
C. Image noise adding
D. Random crop - Answer: BCD
▶ https://www.passquestion.com/h13-311_v4-0-enu.html - By practicing
with PassQuestion H13-311_V4.0-ENU HCIA-AI V4.0 exam questions, you
can significantly reduce preparation time, avoid unnecessary study detours,
and confidently pass the HCIA-AI V4.0 exam on your first attempt.
▶▶ During neural network training, which of the following values is
continuously updated using the gradient descent method to minimize the
loss function?
A. Hyperparameters
B. Feature value
C. Number of samples
D. Parameters - Answer: D
▶ Without considering any regularization terms, the support vectors of a
support vector machine (SVM) are composed of ( ).
A. Points on the separating hyperplane
B. The points farthest from the separating hyperplane
C. The points closest to the separating hyperplane
D. Points of a certain type - Answer: C
▶ Within the broad landscape of large language models built on the
Transformer architecture, which roadmap does GPT belong to?
A. Encoder-Only
B. Decoder-Only
C. Encoder-Decoder
D. Decoder-Encoder - Answer: B
PAPER 2026 COMPLETE QUESTIONS AND
CORRECT ANSWERS GRADED A+
▶ What are the layers used in Transformer to capture time dependencies
within a sequence? (Multiple choice)
A. Residual layer
B. Self-attention layer
C. Location code
D. Feed-forward network layer - Answer: BC
▶ Which of the following hyperparameters may be defined during model
training? (Multiple choice)
A. Batch size
B. Learning rate
C. Optimizer type
D. Loss value - Answer: ABC
▶ Which of the following are common evaluation metrics for classification
tasks? (Multiple choice)
A. Accuracy
B. Precision
C. Recall rate
D. Mean squared error - Answer: ABC
▶ Which of the following statements about data preprocessing are true?
(Multiple choice)
A. Data cleansing is a process of filling in missing values, as well as
detecting and eliminating noise and exceptions.
B. Data dimension reduction aims to simplify data attributes and avoid the
curse of dimensionality.
C. Data standardization aims to reduce noise data and improve model
accuracy by standardizing data.
D. Machine learning tasks generate results in the form of model outputs.
Therefore, model training is more important than data preprocessing. -
Answer: ABC
, ▶ Which of the following statements about model parameters and
hyperparameters are true? (Multiple choice)
A. Models contain both parameters and hyperparameters.
B. Hyperparameters are automatically learned by models.
C. Hyperparameters are manually set.
D. Hyperparameters can be used to control training. - Answer: ACD
▶ Data augmentation can improve model robustness and avoid overfitting.
Which of the following are data augmentation methods? (Multiple choice)
A. Batch normalization
B. Image contrast change
C. Image noise adding
D. Random crop - Answer: BCD
▶ https://www.passquestion.com/h13-311_v4-0-enu.html - By practicing
with PassQuestion H13-311_V4.0-ENU HCIA-AI V4.0 exam questions, you
can significantly reduce preparation time, avoid unnecessary study detours,
and confidently pass the HCIA-AI V4.0 exam on your first attempt.
▶▶ During neural network training, which of the following values is
continuously updated using the gradient descent method to minimize the
loss function?
A. Hyperparameters
B. Feature value
C. Number of samples
D. Parameters - Answer: D
▶ Without considering any regularization terms, the support vectors of a
support vector machine (SVM) are composed of ( ).
A. Points on the separating hyperplane
B. The points farthest from the separating hyperplane
C. The points closest to the separating hyperplane
D. Points of a certain type - Answer: C
▶ Within the broad landscape of large language models built on the
Transformer architecture, which roadmap does GPT belong to?
A. Encoder-Only
B. Decoder-Only
C. Encoder-Decoder
D. Decoder-Encoder - Answer: B
