HCIA-AI V3.5 Certification H13-311_V3.5 Dumps

The H13-311_V3.5 exam is the latest and most updated version for the HCIA-AI V3.5 Certification. This certification is essential for those looking to advance their career in the field of artificial intelligence. Passcert has newly released comprehensive HCIA-AI V3.5 Certification H13-311_V3.5 Dumps that will allow you to prepare for the exam in a more thorough and effective way. These HCIA-AI V3.5 Certification H13-311_V3.5 Dumps include detailed questions and answers, practice tests, and study guides that cover all the key topics and concepts. If you are diligently going through all of our HCIA-AI V3.5 Certification H13-311_V3.5 Dumps, then you will be well-equipped and confident to clear the H13-311_V3.5 exam on the first attempt, ensuring your success and paving the way for future opportunities in the AI field.

HCIA-AI V3.5 CertificationPassing the HCIA-AI V3.5 certification will indicate that you: (1) Understand the AI development history, Huawei Ascend AI system, Huawei full-stack all-scenario AI strategy, cutting-edge AI applications, and algorithms related to traditional machine learning and deep learning (2) Be able to build, train, and deploy neural networks by using the MindSpore development framework (3) Be ready to take on AI positions in sales, marketing, product management, project management, technical support, and more

Target AudiencePersonnel who hope to become AI engineersPersonnel who hope to obtain an HCIA-AI certificatePersonnel who hope to know how to use, manage, and maintain Huawei AI products and AI services

PrerequisitesPossess the basic knowledge of advanced mathematics, and have studied the pre-course of "Math Basics".Familiar with Python language, and have studied the pre-course of "Python Basics".

Huawei HCIA-AI V3.5 Certification Exam OverviewExam CodeH13-311Exam NameHCIA-AIExam LanguageENU/CHSQuestion TypeSingle Answer, Multiple Answer, True-false Question, Fill in the blank answers, Drag and drop itemExam Fees200 USDExam Duration90 minPassing Score600 / 1000

Key Points PercentageKey PointsPercentageAI Overview15%Machine Learning Overview20%Deep Learning Overview25%AI Development Framework20%Introduction to Huawei AI Platforms14%Cutting-edge AI applications6%

AI OverviewAI OverviewApplication Fields of AIHuawei's AI Development StrategyControversies Over AI and Its Future

Machine Learning OverviewMachine Learning AlgorithmsTypes of Machine LearningMachine Learning ProcessImportant Machine Learning ConceptsCommon Machine Learning Algorithms

Deep Learning OverviewDeep LearningTraining RulesActivation FunctionsNormalizationOptimizersNeural Network Types

AI Development FrameworkAI Framework DevelopmentMindSporeMindSpore FeaturesMindSpore Development ComponentsAI Application Development Process

Introduction to Huawei AI PlatformsHuawei Ascend Computing PlatformHuawei Cloud EI PlatformHuawei Device AI Platforms

Cutting-edge AI applicationsReinforcement LearningGANKnowledge GraphIntelligent DrivingQuantum Computing and Machine Learning

Share HCIA-AI V3.5 Certification H13-311_V3.5 Free Dumps1. Which of the following technologies is commonly used for image feature extraction and related research?A. Convolutional neural networkB. Naive Bayes classification algorithmC. Long short-term memory (LSTM) networkD. Word2VecAnswer: A

1. Batch inference is a batch job that performs inference on batch data. There is no need for model training before using batch inference." Which of the following is true about this statement?A. This statement is correct. With batch inference, training is no longer required.B. This statement is correct. Inference means the end of training.C. This statement is incorrect. Model training is required before inference is performed.D. This statement is incorrect. No training is required before batch inference.Answer: C
2. Consider a scenario where a machine learning algorithm is used to filter spam. According to the definition of machine learning, which of the following describes the experience E?A. Spam filteringB. Accuracy of spam filteringC. All tagged spam and genuine emails in the past three yearsD. Email addressesAnswer: C
3. A computer uses labeled images to learn and determine which images contain apples and which contain pears. Which of the following types of machine learning is most applicable to this scenario?A. Supervised learningB. Unsupervised learningC. Semi-supervised learningD. Reinforcement learningAnswer: A
4. Which of the following statements is true about classification models and regression models in machine learning?A. For regression problems, the output variables are discrete values. For classification problems, the output variables are continuous values. B. The most commonly used indicators for evaluating regression and classification problems are accuracy and recall rate.C. There may be overfitting in both regression and classification problems.D. Logistic regression is a typical regression model.Answer: C
5. During neural network training, which of the following values is continuously updated by using the gradient descent method to minimize the loss function?A. HyperparameterB. FeatureC. Number of samplesD. ParameterAnswer: D
6. Which of the following points constitute a support vector of the SVM algorithm without considering regularization terms?A. Points on the separating hyperplaneB. Farthest points from the separating hyperplaneC. Points closest to the separating hyperplaneD. Points of a certain typeAnswer: C
7. Which of the following are false about convolutional neural networks?A. A convolutional neural network may contain convolutional, pooling, and fully connected layers.B. Convolution kernels cannot extract global features of images.C. Common pooling includes max pooling and average pooling.D. When an image is processed, convolution is implemented by using a scanning window.Answer: B
8. Overfitting problems can be avoided through dataset expansion. Which of the following statements is true about dataset expansion?A. The larger the dataset, the lower the probability of overfitting.B. The larger the dataset, the higher the probability of overfitting.C. The smaller the dataset, the lower the probability of overfitting.D. The probability of overfitting decreases when the dataset increases or decreases.Answer: A
9. Which of the following is NOT a complexity feature of Al computing?A. Mixed precision computingB. Parallel data and computingC. Parallel communication and computingD. Parallel processing of structured and unstructured dataAnswer: D
10. Which of the following statements about the running process of the MindArmour subsystem is false?A. Configuration policies: Define test policies based on threat vectors and trustworthiness certification requirements and select appropriate test data generation methods.B. Fuzzing execution: Generate trusted test data randomly based on the model coverage and configuration policies.C. Evaluation report: Generate, an evaluation report based on built-in or user-defined trustworthiness metrics.D. Trustworthiness enhancement: Use preset methods to enhance the trustworthiness of Al models.Answer: B
11. Which of the following are topics of speech processing research?A. Speech recognitionB. Voice processingC. Speech wake-upD. Voiceprint recognitionAnswer: ABCD

Passcert is a professional site to offer you valid questions and answers for best preparation.