As we understand more about machine learning, we will find that its scope is constantly changing
over time.
A
Explanation:
Machine learning is a rapidly evolving field, and its scope indeed changes over time. With
advancements in computational power, the introduction of new algorithms, frameworks, and
techniques, and the growing availability of data, the capabilities of machine learning have expanded
significantly. Initially, machine learning was limited to simpler algorithms like linear regression,
decision trees, and k-nearest neighbors. Over time, however, more complex approaches such as
deep learning and reinforcement learning have emerged, dramatically increasing the applications
and effectiveness of machine learning solutions.
In the Huawei HCIA-AI curriculum, it is emphasized that AI, especially machine learning, has become
more powerful due to these continuous developments, allowing it to be applied to broader and
more complex problems. The framework and methodologies in machine learning have evolved,
making it possible to perform more sophisticated tasks such as real-time decision-making, image
recognition, natural language processing, and even autonomous driving.
As technology advances, the scope of machine learning will continue to shift, providing new
opportunities for innovation. This is why it is important to stay updated on recent developments to
fully leverage machine learning in various AI applications.
Reference: Huawei HCIA-AI Certification, Machine Learning Overview.
As the cornerstone of Huawei's full-stack, all-scenario AI solution, it provides modules, boards, and
servers powered by the Ascend AI processor to meet customer demand for computing power in all
scenarios.
A
Explanation:
Atlas is a key part of Huawei's full-stack, all-scenario AI solution. It provides AI hardware resources in
the form of modules, boards, edge stations, and servers powered by Huawei's Ascend AI processors.
The Atlas series is designed to meet customer demands for AI computing power in a variety of
deployment scenarios, including cloud, edge, and device environments.
Huawei’s full-stack AI solution aims to deliver comprehensive AI capabilities across different levels.
The Atlas series supports a wide range of industries by offering scalable AI computing resources,
which are critical for industries dealing with large volumes of data and needing high-performance
computing.
Reference: Huawei HCIA-AI Certification, Introduction to Huawei AI Platforms.
Huawei's full-stack AI solution includes Ascend, MindSpore, and ModelArts. (Enter an acronym.)
C
Explanation:
CANN (Compute Architecture for Neural Networks) is part of Huawei's full-stack AI solution, which
includes Ascend (hardware), MindSpore (AI framework), and ModelArts (AI development platform).
CANN optimizes the computing efficiency of AI models and provides basic software components for
the Ascend AI processors. This architecture supports deep learning and machine learning tasks by
enhancing computational performance and providing better neural network training efficiency.
Together, Ascend, MindSpore, and CANN form a critical infrastructure that underpins Huawei’s AI
development ecosystem, allowing seamless integration from hardware to software.
Reference: Huawei HCIA-AI Certification, AI Development Framework and Platforms.
The concept of "artificial intelligence" was first proposed in the year of:
B
Explanation:
The concept of "artificial intelligence" was first formally introduced in 1956 during the Dartmouth
Conference, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon.
This event is widely regarded as the birth of AI as a field of study. The conference aimed to explore
the idea that human intelligence could be simulated by machines, laying the groundwork for
subsequent AI research and development.
This date is significant in the history of AI because it marked the beginning of a concentrated effort to
develop machines that could mimic cognitive functions such as learning, reasoning, and problem-
solving.
Reference: Huawei HCIA-AI Certification, AI Overview.
Which of the following are subfields of AI?
B, D
Explanation:
Artificial intelligence is a broad field that encompasses several subfields. Two key subfields are:
Expert systems, which are computer programs that mimic the decision-making abilities of a human
expert by reasoning through bodies of knowledge. These systems are used in various domains such
as healthcare, engineering, and finance.
Computer vision, which enables machines to interpret and understand visual data from the world. It
includes tasks such as object detection, image recognition, and video analysis.
While options like backpropagation and smart finance are related to AI, they represent specific
algorithms or application areas rather than broad subfields.
Reference: Huawei HCIA-AI Certification, AI Overview and Applications.
What are the application scenarios of computer vision?
A, B, C
Explanation:
Computer vision, a subfield of AI, has various application scenarios that involve the analysis and
understanding of images and videos. Some key application scenarios include:
Video action analysis: Identifying and analyzing human actions or movements in videos.
Image search: Using visual information to search for similar images in large databases.
Smart albums: Organizing and categorizing photos using AI-based image recognition algorithms to
group them by themes, people, or events.
Voice navigation is a part of natural language processing and speech recognition, not computer
vision.
Reference: Huawei HCIA-AI Certification, AI Applications in Computer Vision.
Which of the following is NOT a commonly used AI computing framework?
D
Explanation:
OpenCV is a library used primarily for computer vision tasks like image and video processing. It is not
considered an AI computing framework in the same way as PyTorch, MindSpore, or TensorFlow,
which are commonly used frameworks for developing AI and machine learning models. AI
frameworks like PyTorch, TensorFlow, and Huawei’s MindSpore are designed to facilitate the
development and deployment of deep learning models.
Reference: Huawei HCIA-AI Certification, AI Development Frameworks.
"Today's speech processing technology can achieve a recognition accuracy of over 90% in any case."
Which of the following is true about this statement?
B
Explanation:
While speech recognition technology has improved significantly, its accuracy can still be affected by
external factors such as noise, background sound, accents, and speech clarity. Although systems can
achieve over 90% accuracy under controlled conditions, the accuracy drops in noisy or complex real-
world environments. Therefore, the statement that today's speech processing technology can always
achieve high recognition accuracy is incorrect.
Speech recognition systems are sophisticated but still face challenges in environments with heavy
noise, where the technology has difficulty interpreting speech accurately.
Reference: Huawei HCIA-AI Certification, AI Applications in Speech Processing.
"AI application fields include only computer vision and speech processing." Which of the following is
true about this statement?
A
Explanation:
AI is not limited to just computer vision and speech processing. In addition to these fields, AI
encompasses other important areas such as natural language processing (NLP), robotics, smart
finance, autonomous driving, and more. Natural language processing focuses on understanding and
generating human language, while other fields apply AI to various industries and applications such as
healthcare, finance, and manufacturing. AI is a broad field with numerous application areas.
Reference: Huawei HCIA-AI Certification, AI Overview and Applications.
Which of the following are common gradient descent methods?
A, B, D
Explanation:
The gradient descent method is a core optimization technique in machine learning, particularly for
neural networks and deep learning models. The common gradient descent methods include:
Batch Gradient Descent (BGD): Updates the model parameters after computing the gradients from
the entire dataset.
Mini-batch Gradient Descent (MBGD): Updates the model parameters using a small batch of data,
combining the benefits of both batch and stochastic gradient descent.
Stochastic Gradient Descent (SGD): Updates the model parameters for each individual data point,
leading to faster but noisier updates.
Multi-dimensional gradient descent is not a recognized method in AI or machine learning.
Reference: Huawei HCIA-AI Certification, Machine Learning Algorithms.
Which of the following algorithms presents the most chaotic landscape on the loss surface?
A
Explanation:
Stochastic Gradient Descent (SGD) presents the most chaotic landscape on the loss surface because it
updates the model parameters for each individual training example, which can introduce a significant
amount of noise into the optimization process. This leads to a less smooth and more chaotic path
toward the global minimum compared to methods like batch gradient descent or mini-batch gradient
descent, which provide more stable updates.
Reference: Huawei HCIA-AI Certification, Machine Learning Algorithms.
Which of the following statements are true about the k-nearest neighbors (k-NN) algorithm?
B, D
Explanation:
The k-nearest neighbors (k-NN) algorithm is a non-parametric algorithm used for both classification
and regression. In classification tasks, it typically uses majority voting to assign a label to a new
instance based on the most common class among its nearest neighbors. The algorithm works by
calculating the distance (often using Euclidean distance) between the query point and the points in
the dataset, and then assigning the query point to the class that is most frequent among its k nearest
neighbors.
For regression tasks, k-NN can predict the outcome based on the mean of the values of the k nearest
neighbors, although this is less common than its classification use.
Reference: Huawei HCIA-AI Certification, Machine Learning Overview.
An algorithm of unsupervised learning classifies samples in a dataset into several categories.
Samples belonging to the same category have high similarity.
A
Explanation:
In unsupervised learning, the goal is to find hidden patterns or intrinsic structures in input data
without labeled outcomes. One common unsupervised learning task is clustering, where an
algorithm groups the dataset into several categories or clusters. Samples within the same cluster
have high similarity based on certain features, while samples in different clusters have low similarity.
Examples of clustering algorithms include k-means and hierarchical clustering.
Reference: Huawei HCIA-AI Certification, Machine Learning Overview.
Which of the following statements is false about the debugging and application of a regression
model?
D
Explanation:
Logistic regression is not a solution for underfitting in regression models, as it is used primarily for
classification problems rather than regression tasks. If underfitting occurs, it means that the model is
too simple to capture the underlying patterns in the data. Solutions include using a more complex
regression model like polynomial regression or increasing the number of features in the dataset.
Other options like adding a regularization term for overfitting (Lasso or Ridge) and using data
cleansing and feature engineering are correct methods for improving model performance.
Reference: Huawei HCIA-AI Certification, AI Model Debugging and Optimization.
In machine learning, which of the following inputs is required for model training and prediction?
B
Explanation:
In machine learning, historical data is crucial for model training and prediction. The model learns
from this data, identifying patterns and relationships between features and target variables. While
the training algorithm is necessary for defining how the model learns, the input required for the
model is historical data, as it serves as the foundation for training the model to make future
predictions.
Neural networks and training algorithms are parts of the model development process, but they are
not the actual input for model training.
Reference: Huawei HCIA-AI Certification, Machine Learning Workflow.