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Which statement best describes the relationship between AI, machine learning (ML), and deep learning (DL)?

AI, ML, and DL are three independent fields with no overlap

ML is a subset of AI, and DL is a subset of ML

DL is a subset of AI, and ML is a subset of DL

AI is a subset of ML, and ML is a subset of DL

to track

2026 Statistics

Key Facts: Huawei HCIA-AI Exam

Exam Questions

Huawei

90 min

Time Limit

Huawei

600/1000

Passing Score

Huawei

$200

Exam Fee

Pearson VUE

3 Years

Certification Validity

Huawei

H13-311 V3.5

Current Exam Code

Huawei

HCIA-AI (H13-311 V3.5) has 60 questions in 90 minutes with a 600/1000 scaled passing score. The US$200 exam covers AI/ML/DL fundamentals, key algorithms, model evaluation, deep learning architectures (CNN, RNN, Transformer), and the full Huawei AI stack: MindSpore, CANN, Ascend NPUs, Atlas devices, ModelArts, Huawei Cloud AI services, AI ethics, and generative AI / Pangu LLMs. Certification is valid for 3 years.

Sample Huawei HCIA-AI Practice Questions

Try these sample questions to test your Huawei HCIA-AI exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1Which statement best describes the relationship between AI, machine learning (ML), and deep learning (DL)?

A.AI, ML, and DL are three independent fields with no overlap

B.ML is a subset of AI, and DL is a subset of ML

C.DL is a subset of AI, and ML is a subset of DL

D.AI is a subset of ML, and ML is a subset of DL

Explanation: Artificial intelligence is the broadest field aiming to make machines simulate human intelligence. Machine learning is a subset of AI that learns patterns from data without being explicitly programmed. Deep learning is a subset of machine learning that uses multi-layer neural networks. The standard hierarchy taught in HCIA-AI is AI then ML then DL, from broadest to narrowest.

2Which of the following is the best example of narrow (weak) AI rather than general (strong) AI?

A.A system that can perform any intellectual task a human can

B.A speech-to-text engine that transcribes English audio

C.A self-aware machine with consciousness

D.An AI that can learn any new domain without retraining

Explanation: Narrow AI is designed for a specific task and cannot transfer its abilities to unrelated tasks. A speech-to-text engine is a classic narrow AI example. General AI (AGI) would match human cognitive ability across any domain, which has not yet been achieved. All AI systems available today, including those on Huawei Cloud, are narrow AI.

3Which event is widely cited as marking the formal birth of artificial intelligence as a research field?

A.The 1956 Dartmouth Workshop

B.IBM Deep Blue defeating Kasparov in 1997

C.AlexNet winning ImageNet in 2012

D.The release of ChatGPT in 2022

Explanation: The 1956 Dartmouth Summer Research Project, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, is universally considered the founding event of AI as a discipline. The term artificial intelligence was coined for this workshop. The other events are all major milestones in AI history but came decades later.

4Which of the following is NOT a core application domain of modern AI as covered in HCIA-AI?

A.Computer vision (CV)

B.Natural language processing (NLP)

C.Speech processing

D.Mechanical assembly torque calibration

Explanation: HCIA-AI categorizes AI's main application domains as computer vision, natural language processing, speech processing, and recommendation systems. Mechanical torque calibration is a manufacturing engineering activity, not an AI application domain in itself, although AI can support smart manufacturing more broadly.

5Which type of machine learning task uses labeled data to predict a continuous numerical output, such as a house price?

A.Classification

B.Regression

C.Clustering

D.Reinforcement learning

Explanation: Regression is a supervised learning task that predicts a continuous numeric value from labeled training data. Predicting house prices, temperature, or sales volume are textbook regression problems. Classification predicts discrete categories. Clustering is unsupervised. Reinforcement learning learns from reward signals, not from labeled outputs.

6Which algorithm is most commonly used as the baseline for binary classification problems and outputs class probabilities via the sigmoid function?

A.Linear regression

B.K-means

C.Logistic regression

D.DBSCAN

Explanation: Logistic regression is a supervised classification algorithm that applies the sigmoid (logistic) function to a linear combination of inputs to produce a probability between 0 and 1. It is the standard baseline for binary classification. Linear regression predicts continuous values, while K-means and DBSCAN are unsupervised clustering methods.

7Which of the following is an unsupervised learning algorithm?

A.Random forest

B.Support vector machine

C.K-means

D.Logistic regression

Explanation: K-means is an unsupervised clustering algorithm that partitions data into K clusters by iteratively assigning points to the nearest centroid and recomputing centroids. Random forest, SVM, and logistic regression are all supervised learning algorithms that require labeled training data.

8What is the primary purpose of dimensionality reduction techniques such as PCA?

A.To increase the number of features by polynomial expansion

B.To project high-dimensional data onto fewer features while retaining as much variance as possible

C.To cluster data into K groups

D.To label unlabeled training data automatically

Explanation: Principal component analysis (PCA) is a dimensionality reduction technique that finds orthogonal directions of maximum variance and projects data onto a smaller set of components. It is used to reduce noise, speed up training, and enable visualization. It is unsupervised and does not produce labels or perform clustering directly.

9In reinforcement learning, what does the agent receive from the environment after taking an action?

A.A labeled training example

B.A reward signal and a new state

C.A cluster assignment

D.A backpropagated gradient

Explanation: In reinforcement learning, the agent observes the environment state, takes an action, and receives a reward and a new state. Over many episodes, the agent learns a policy that maximizes cumulative reward. RL does not use labeled examples like supervised learning; it learns from interaction with an environment.

10Which evaluation metric is most appropriate for a highly imbalanced binary classification problem, such as fraud detection where only 1% of cases are positive?

A.Accuracy

B.Mean squared error

C.F1 score (or precision and recall together)

D.R squared

Explanation: On highly imbalanced data, accuracy is misleading because predicting only the majority class can yield high accuracy yet detect zero positives. F1 score combines precision and recall and rewards models that correctly identify the rare positive class. MSE and R squared are regression metrics, not classification metrics.

About the Huawei HCIA-AI Exam

The Huawei Certified ICT Associate - Artificial Intelligence (HCIA-AI, exam code H13-311 V3.5) validates foundational knowledge of AI, machine learning, and deep learning, plus hands-on familiarity with the Huawei AI ecosystem — MindSpore framework, Ascend AI processors (310 / 910), the Atlas hardware lineup (Atlas 200/300/500/800/900), CANN, ModelArts, and Huawei Cloud AI services. It targets engineers, students, and ICT professionals starting an AI career path within Huawei's ecosystem.

Questions

60 scored questions

Time Limit

90 minutes

Passing Score

600/1000

Exam Fee

$200 USD (Huawei / Pearson VUE)

Huawei HCIA-AI Exam Content Outline

AI Overview

AI history, narrow vs general AI, AI / ML / DL relationship, four key application domains (CV, NLP, speech, recommendation)

20%

Machine Learning

Supervised, unsupervised, and reinforcement learning; key algorithms (linear / logistic regression, decision tree, random forest, SVM, KNN, K-means, DBSCAN, naive Bayes, gradient boosting); ML lifecycle

10%

Model Evaluation & Tuning

Train / validation / test split, K-fold CV, accuracy / precision / recall / F1, ROC / AUC, MSE / R squared, overfitting / underfitting, L1 / L2, dropout, early stopping

22%

Deep Learning Fundamentals

Perceptron, MLP, activations (ReLU, sigmoid, tanh, softmax), forward / backward propagation, loss functions, SGD / Adam / RMSprop, batch normalization, regularization

10%

Neural Network Architectures

CNN (conv, pool, ResNet, VGG, Inception), RNN / LSTM / GRU, sequence-to-sequence, Transformer self-attention basics

20%

Huawei AI Ecosystem

MindSpore framework, Ascend 310 / 910 NPUs, Atlas 200 / 300 / 500 / 800 / 900 hardware, CANN compute architecture, ModelArts (Data / Train / Deploy / Inference / ExeML AutoML)

Huawei Cloud AI Services

Image Recognition, OCR, Speech Interaction Service (ASR/TTS), NLP, Conversational Bot Service, Knowledge Graph

AI Applications, Ethics & Generative AI

Smart city / manufacturing / healthcare / transportation, bias / fairness / GDPR / responsible AI, LLM basics, prompt engineering, foundation models, Pangu L0 / L1 / L2 family

How to Pass the Huawei HCIA-AI Exam

What You Need to Know

Passing score: 600/1000
Exam length: 60 questions
Time limit: 90 minutes
Exam fee: $200 USD

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

Huawei HCIA-AI Study Tips from Top Performers

1Master the AI / ML / DL hierarchy and the four AI application domains (CV, NLP, speech, recommendation) — they are tested directly and underlie many scenarios

2Memorize the Huawei AI stack from bottom to top: Ascend chips → CANN → MindSpore / ModelArts → Huawei Cloud AI services and Pangu LLMs

3Know the Atlas product map cold: Atlas 200 DK (developer kit), Atlas 300I / 300T (PCIe cards), Atlas 500 (edge), Atlas 800 (server), Atlas 900 (training cluster)

4Distinguish Ascend 310 (inference, low-power) from Ascend 910 (training, high-performance) — every exam has questions on this split

5Practice writing out classical metrics (accuracy, precision, recall, F1, ROC / AUC, MSE / R squared) by hand and know which apply to classification vs regression

Frequently Asked Questions

What is the Huawei HCIA-AI passing score?

HCIA-AI (H13-311 V3.5) requires a scaled score of 600 out of 1000 to pass. The exam contains 60 questions delivered in 90 minutes through Pearson VUE, giving you about 90 seconds per question. Question types include single-answer multiple choice, multiple-answer multiple choice, true / false, and drag-and-drop matching.

How much does the Huawei HCIA-AI exam cost?

The HCIA-AI exam fee is US$200 globally when registering through Pearson VUE. Local pricing may differ in some regions. Huawei occasionally offers training vouchers via Huawei Talent Online, Huawei ICT Academy, and partner programs that can reduce or cover the exam fee.

What topics are on the HCIA-AI H13-311 V3.5 exam?

The exam covers AI overview and history, machine learning (supervised, unsupervised, reinforcement) with classical algorithms, model evaluation and tuning, deep learning fundamentals, key neural network architectures (CNN, RNN / LSTM, Transformer), the Huawei AI stack (MindSpore, CANN, Ascend NPUs, Atlas hardware, ModelArts), Huawei Cloud AI services, AI ethics, and generative AI fundamentals including the Pangu LLM family.

How long is the Huawei HCIA-AI certification valid?

HCIA-AI certification is valid for 3 years from the date you pass. To recertify, you can either retake the current HCIA-AI exam, pass a higher-level Huawei AI exam (HCIP-AI or HCIE-AI), or complete Huawei's recertification path before expiration.

Do I need programming experience for HCIA-AI?

Some Python familiarity helps — official labs use Python with MindSpore, NumPy, and pandas — but the H13-311 V3.5 exam itself is concept-focused and does not require you to write code from scratch in the test. You should be comfortable reading short Python / MindSpore snippets and understanding common ML and deep learning vocabulary.

How long should I study for the HCIA-AI exam?

Most candidates need 4 to 8 weeks at roughly 8 to 12 hours per week, totaling 40 to 80 hours. If you already have ML / deep learning experience, focus mainly on the Huawei-specific content (MindSpore, Ascend, CANN, Atlas, ModelArts, Pangu). Aim to score consistently above 80% on full-length practice tests before booking the real exam.

Is HCIA-AI worth it in 2026?

HCIA-AI is most valuable if you work with Huawei products, target a Chinese / APAC / Middle East market, or want a structured AI fundamentals path that also covers a major non-US AI ecosystem (MindSpore, Ascend, ModelArts, Pangu). It pairs well with vendor-neutral certifications and is a recognized stepping stone toward HCIP-AI and HCIE-AI.