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About the test:

计算机视觉测试评估了候选人对计算机视觉技术的知识和理解,包括深度学习和机器学习算法。它评估图像识别,对象检测,图像分割和特征提取方面的技能。

Covered skills:

  • 图像识别
  • 图像分割
  • 卷积神经网络
  • 图像分类
  • 机器学习
  • 简历框架
  • 对象检测
  • 特征提取
  • 神经网络
  • 深度学习
  • Python

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9 reasons why
9 reasons why

Adaface Computer Vision Assessment Test is the most accurate way to shortlist 计算机视觉工程师s



Reason #1

Tests for on-the-job skills

The Computer Vision Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

  • 执行图像识别任务的能力
  • 能够检测图像中的对象
  • 能够准确细分图像
  • 能够从图像中提取功能
  • 与卷积神经网络(CNN)合作的能力
  • 能够建立用于计算机视觉任务的神经网络
  • 使用机器学习技术对图像进行分类的能力
  • 能够将深度学习原则应用于计算机视觉
  • 能够将机器学习算法应用于计算机视觉问题
  • 在Python中编码计算机视觉任务的能力
  • 各种计算机视觉框架的知识
Reason #2

No trick questions

no trick questions

Traditional assessment tools use trick questions and puzzles for the screening, which creates a lot of frustration among candidates about having to go through irrelevant screening assessments.

View sample questions

The main reason we started Adaface is that traditional pre-employment assessment platforms are not a fair way for companies to evaluate candidates. At Adaface, our mission is to help companies find great candidates by assessing on-the-job skills required for a role.

Why we started Adaface
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Reason #3

Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

这些只是我们库中有10,000多个问题的一个小样本。关于此的实际问题 计算机视觉测试 将是不可行的.

🧐 Question

Medium

Changed decision boundary
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We have trained a model on a linearly separable training set to classify the data points into 2 sets (binary classification). Our intern recently labelled some new data points which are all correctly classified by the model. All of the new data points lie far away from the decision boundary. We added these new data points and re-trained our model- our decision boundary changed. Which of these models do you think we could be working with?
The 2 data sources use SQL Server and have a 3-character CompanyCode column. Both data sources contain an ORDER BY clause to sort the data by CompanyCode in ascending order. 

Teylor wants to make sure that the Merge Join transformation works without additional transformations. What would you recommend?
A: Perceptron
B: SVM
C: Logistic regression
D: Guassion discriminant analysis

Medium

CNN Architecture Tuning
Convolutional Neural Networks
Hyperparameter Optimization
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You are fine-tuning a Convolutional Neural Network (CNN) for image classification. The network architecture is as follows:
 image
The model is trained using the following parameters:

- Batch size: 64
- Learning rate: 0.001
- Optimizer: Adam
- Loss function: Categorical cross-entropy

After several training epochs, you observe that the training accuracy is high, but the validation accuracy plateaus and is significantly lower. This suggests possible overfitting. Which of the following adjustments would most effectively mitigate this issue without overly compromising the model's performance?
A: Increase the batch size to 128
B: Add dropout layers with a dropout rate of 0.5 after each MaxPooling2D layer
C: Replace Adam optimizer with SGD (Stochastic Gradient Descent)
D: Decrease the number of filters in each Conv2D layer by half
E: Increase the learning rate to 0.01
F: Reduce the size of the Dense layer to 64 units

Medium

CNN for Imbalanced Image Dataset
Convolutional Neural Networks
Imbalanced Datasets
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You are fine-tuning a Convolutional Neural Network (CNN) for an image classification task where the dataset is highly imbalanced. The majority class comprises 70% of the data. The initial model setup and subsequent experiments yield the following observations:

**Initial Setup:**

- CNN architecture: 6 convolutional layers with increasing filter sizes, followed by 2 fully connected layers.
- Activation function: ReLU
- No class-weighting or data augmentation.
- Results: High overall accuracy, but poor precision and recall for minority classes.

**Experiment 1:**

- Changes: Implement class-weighting to penalize mistakes on minority classes more heavily.
- Results: Improved precision and recall for minority classes, but overall accuracy slightly decreased.

**Experiment 2:**

- Changes: Add dropout layers with a rate of 0.5 after each convolutional layer.
- Results: Overall accuracy decreased, and no significant change in precision and recall for minority classes.

Given these outcomes, what is the most effective strategy to further improve the model's performance specifically for the minority classes without compromising the overall accuracy?
A: Increase the dropout rate to 0.7
B: Further fine-tune class-weighting parameters
C: Increase the number of filters in the convolutional layers
D: Add batch normalization layers after each convolutional layer
E: Use a different activation function like LeakyReLU
F: Implement more aggressive data augmentation on the minority class

Easy

Gradient descent optimization
Gradient Descent
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You are working on a regression problem using a simple neural network. You want to optimize the model's weights using gradient descent with different learning rate schedules. Consider the following pseudo code for training the neural network:
 image
Which of the following learning rate schedules would most likely result in the fastest convergence without overshooting the optimal weights?

A: Constant learning rate of 0.01
B: Exponential decay with initial learning rate of 0.1 and decay rate of 0.99
C: Exponential decay with initial learning rate of 0.01 and decay rate of 0.99
D: Step decay with initial learning rate of 0.1 and decay rate of 0.5 every 100 epochs
E: Step decay with initial learning rate of 0.01 and decay rate of 0.5 every 100 epochs
F: Constant learning rate of 0.1

Medium

Less complex decision tree model
Model Complexity
Overfitting
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You are given a dataset to solve a classification problem using a decision tree algorithm. You are concerned about overfitting and decide to implement pruning to control the model's complexity. Consider the following pseudo code for creating the decision tree model:
 image
Which of the following combinations of parameters would result in a less complex decision tree model, reducing the risk of overfitting?

A: max_depth=5, min_samples_split=2, min_samples_leaf=1
B: max_depth=None, min_samples_split=5, min_samples_leaf=5
C: max_depth=3, min_samples_split=2, min_samples_leaf=1
D: max_depth=None, min_samples_split=2, min_samples_leaf=1
E: max_depth=3, min_samples_split=10, min_samples_leaf=10
F; max_depth=5, min_samples_split=5, min_samples_leaf=5

Easy

n-gram generator
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Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
 image

Easy

Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering
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You are tasked with building a recommendation system for a newly launched e-commerce website. Given that the website is new, there is not much user interaction data available. Also, the items in the catalog have rich descriptions. Based on these requirements, which type of recommendation system approach would be the most suitable for this task?

Easy

Sensitivity and Specificity
Confusion Matrix
Model Evaluation
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You have trained a supervised learning model to classify customer reviews as either "positive" or "negative" based on a dataset with 10,000 samples and 35 features, including the review text, reviewer's name, and rating. The dataset is split into a 7,000-sample training set and a 3,000-sample test set.

After training the model, you evaluate its performance using a confusion matrix on the test set, which shows the following results:
 image
Based on the confusion matrix, what are the sensitivity and specificity of the model?

Medium

ZeroDivisionError and IndexError
Exceptions
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What will the following Python code output?
 image

Medium

Session
File Handling
Dictionary
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 image
The function high_sess should compute the highest number of events per session of each user in the database by reading a comma-separated value input file of session data. The result should be returned from the function as a dictionary. The first column of each line in the input file is expected to contain the user’s name represented as a string. The second column is expected to contain an integer representing the events in a session. Here is an example input file:
Tony,10
Stark,12
Black,25
Your program should ignore a non-conforming line like this one.
Stark,3
Widow,6
Widow,14
The resulting return value for this file should be the following dictionary: { 'Stark':12, 'Black':25, 'Tony':10, 'Widow':14 }
What should replace the CODE TO FILL line to complete the function?
 image

Medium

Max Code
Arrays
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Below are code lines to create a Python function. Ignoring indentation, what lines should be used and in what order for the following function to be complete:
 image

Medium

Recursive Function
Recursion
Dictionary
Lists
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Consider the following Python code:
 image
In the above code, recursive_search is a function that takes a dictionary (data) and a target key (target) as arguments. It searches for the target key within the dictionary, which could potentially have nested dictionaries and lists as values, and returns the value associated with the target key. If the target key is not found, it returns None.

nested_dict is a dictionary that contains multiple levels of nested dictionaries and lists. The recursive_search function is then called with nested_dict as the data and 'target_key' as the target.

What will the output be after executing the above code?

Medium

Stacking problem
Stack
Linkedlist
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What does the below function ‘fun’ does?
 image
A: Sum of digits of the number passed to fun.
B: Number of digits of the number passed to fun.
C: 0 if the number passed to fun is divisible by 10. 1 otherwise.
D: Sum of all digits number passed to fun except for the last digit.
🧐 Question🔧 Skill

Medium

Changed decision boundary

2 mins

Deep Learning
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Medium

CNN Architecture Tuning
Convolutional Neural Networks
Hyperparameter Optimization

3 mins

Deep Learning
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Medium

CNN for Imbalanced Image Dataset
Convolutional Neural Networks
Imbalanced Datasets

3 mins

Deep Learning
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Easy

Gradient descent optimization
Gradient Descent

2 mins

Machine Learning
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Medium

Less complex decision tree model
Model Complexity
Overfitting

2 mins

Machine Learning
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Easy

n-gram generator

2 mins

Machine Learning
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Easy

Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering

2 mins

Machine Learning
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Easy

Sensitivity and Specificity
Confusion Matrix
Model Evaluation

2 mins

Machine Learning
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Medium

ZeroDivisionError and IndexError
Exceptions

2 mins

Python
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Medium

Session
File Handling
Dictionary

2 mins

Python
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Medium

Max Code
Arrays

2 mins

Python
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Medium

Recursive Function
Recursion
Dictionary
Lists

3 mins

Python
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Medium

Stacking problem
Stack
Linkedlist

4 mins

Python
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🧐 Question🔧 Skill💪 Difficulty⌛ Time
Changed decision boundary
Deep Learning
Medium2 mins
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CNN Architecture Tuning
Convolutional Neural Networks
Hyperparameter Optimization
Deep Learning
Medium3 mins
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CNN for Imbalanced Image Dataset
Convolutional Neural Networks
Imbalanced Datasets
Deep Learning
Medium3 mins
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Gradient descent optimization
Gradient Descent
Machine Learning
Easy2 mins
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Less complex decision tree model
Model Complexity
Overfitting
Machine Learning
Medium2 mins
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n-gram generator
Machine Learning
Easy2 mins
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Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering
Machine Learning
Easy2 mins
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Sensitivity and Specificity
Confusion Matrix
Model Evaluation
Machine Learning
Easy2 mins
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ZeroDivisionError and IndexError
Exceptions
Python
Medium2 mins
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Session
File Handling
Dictionary
Python
Medium2 mins
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Max Code
Arrays
Python
Medium2 mins
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Recursive Function
Recursion
Dictionary
Lists
Python
Medium3 mins
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Stacking problem
Stack
Linkedlist
Python
Medium4 mins
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Reason #4

1200+ customers in 75 countries

customers in 75 countries
Brandon

借助 Adaface,我们能够将初步筛选流程优化高达 75% 以上,为招聘经理和我们的人才招聘团队节省了宝贵的时间!


Brandon Lee, 人事主管, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment 计算机视觉测试 in your hiring process is that it is an elimination tool, not a selection tool. In other words: you want to use the test to eliminate the candidates who do poorly on the test, not to select the candidates who come out at the top. While they are super valuable, pre-employment tests do not paint the entire picture of a candidate’s abilities, knowledge, and motivations. Multiple easy questions are more predictive of a candidate's ability than fewer hard questions. Harder questions are often "trick" based questions, which do not provide any meaningful signal about the candidate's skillset.

Science behind Adaface tests
Reason #6

1 click candidate invites

Email invites: You can send candidates an email invite to the 计算机视觉测试 from your dashboard by entering their email address.

Public link: You can create a public link for each test that you can share with candidates.

API or integrations: You can invite candidates directly from your ATS by using our pre-built integrations with popular ATS systems or building a custom integration with your in-house ATS.

invite candidates
Reason #7

Detailed scorecards & benchmarks

查看样本记分卡
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Reason #8

High completion rate

Adaface tests are conversational, low-stress, and take just 25-40 mins to complete.

This is why Adaface has the highest test-completion rate (86%), which is more than 2x better than traditional assessments.

test completion rate
Reason #9

Advanced Proctoring


Learn more

About the Computer Vision Online Test

Why you should use Pre-employment Computer Vision Test?

The 计算机视觉测试 makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

  • 开发计算机视觉应用程序的经验
  • 强大的机器学习算法知识
  • Python编程语言专业知识
  • 精通计算机视觉框架
  • 执行图像识别任务的能力
  • 了解对象检测技术
  • 图像分割方法的知识
  • 从图像中提取功能的经验
  • 熟悉卷积神经网络
  • 精通神经网络体系结构

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

What topics are covered in the Computer Vision Test?

  • 对象检测

    对象检测是一项计算机视觉任务,涉及在图像或视频中查找和本地化对象。在测试中测量了该技能,以评估候选人对检测和定位对象的算法和方法的了解,这在监视,自动驾驶汽车和基于图像的搜索系统等应用中至关重要。

  • 图像段段

    图像分割是将图像将图像划分为多个区域或段的过程,其目的是简化或分析图像的表示形式。测量测试中的这一技能使招聘人员可以评估候选人使用技术和算法进行图像分割的能力,该技术在医学图像分析,对象识别和图像编辑等应用中起着至关重要的作用。

  • 特征特征提取。

    特征提取涉及从图像(例如图像)中得出有意义的信息或特征,以促进后续的分析或分类。在测试中测量了该技能,以评估候选人对计算机视觉中使用的特征提取技术的理解,这对于诸如对象识别,图像匹配和模式分析等任务至关重要。

  • 卷积神经网络</h4 > <p>卷积神经网络(CNN)是专门为处理视觉数据(例如图像)设计的深度学习模型。在测试中测量了该技能,以评估候选人对CNN体系结构的了解,以及他们训练和应用CNN的能力,以进行图像分类,对象检测和图像分割等任务。</p> <h4>神经网络</ H4> <p>神经网络是受人脑的结构和功能启发的计算模型,用于模式识别和机器学习任务。测量测试中的这一技能使招聘人员可以评估候选人对神经网络概念的理解及其应用神经网络解决计算机视觉问题的能力。</p> <h4>图像分类

    图像分类是将标签或类别分配给图像的任务。在测试中测量了该技能,以评估候选人应用于图像的分类算法和技术的知识,这对于各种应用程序至关重要,例如图像搜索,内容过滤和自动图像标记。

  • 深度学习</ H4> <p>深度学习是机器学习的一个子领域,重点是建立和培训具有多层的人工神经网络。测量测试中的这一技能使招聘人员可以评估候选人对深度学习原则的理解及其将深度学习模型应用于图像识别,对象检测和图像生成等任务的能力。</p> <h4>机器学习</h4 > <p>机器学习是人工智能的一个分支,致力于开发能够根据数据学习并做出预测或决策的算法和模型。在测试中测量了该技能,以评估候选人对机器学习概念的理解及其将机器学习技术应用于计算机视觉问题的能力。</p> <h4> Python

    Python是一种流行的编程语言广泛用于计算机视觉和机器学习领域。测量测试中的这一技能使招聘人员可以评估候选人在Python编程中的熟练程度,以及他们使用Python库和框架实施计算机视觉算法和模型的能力。

  • Full list of covered topics

    The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for 计算机视觉测试 to be based on.

    图像识别
    对象检测
    图像分割
    特征提取
    卷积神经网络(CNN)
    神经网络
    图像分类
    深度学习
    机器学习
    Python
    简历框架
    预处理
    激活功能
    损失功能
    优化算法
    数据增强
    转移学习
    反向传播
    正则化
    高参数调整
    交叉验证
    二进制分类
    多类分类
    对象本地化
    边界框回归
    实例细分
    语义细分
    编码器架构
    复发性神经网络(RNN)
    卷积层
    合并层
    完全连接的层
    批量归一化
    辍学
    图像预处理
    数据增强技术
    数据标签
    功能选择
    主成分分析(PCA)
    线性回归
    逻辑回归
    支持向量机(SVM)
    随机森林
    k-nearest邻居(KNN)
    天真的贝叶斯
    模型评估指标
    混淆矩阵
    精确和回忆
    F1得分
    接收器操作特征(ROC)曲线
    AUC-ROC得分
    网格搜索
    k折交叉验证
    合奏学习
    过度拟合和不足
    Python语法
    变量类型
    控制流
    功能
    文件处理
    Python图书馆
    numpy
    熊猫
    matplotlib
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What roles can I use the Computer Vision Test for?

  • 计算机视觉工程师
  • 机器学习工程师
  • 人工智能研究员
  • 数据科学家
  • 软件开发人员
  • 数据分析师
  • 图像处理工程师
  • 研究科学家
  • 计算机视觉顾问
  • 数据工程师

How is the Computer Vision Test customized for senior candidates?

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

  • 图像分类的能力
  • 对深度学习概念的深刻理解
  • 机器学习算法专业知识
  • 精通Python编程语言
  • 与计算机视觉框架合作的能力
  • 实施图像识别模型的经验
  • 对物体检测原理的强烈掌握
  • 高级图像分割技术的知识
  • 熟练特征提取方法
  • 深入了解卷积神经网络
Singapore government logo

招聘经理认为,通过小组面试中提出的技术问题,他们能够判断哪些候选人得分更高,并与得分较差的候选人区分开来。他们是 非常满意 通过 Adaface 筛选入围的候选人的质量。


85%
减少筛查时间

Computer Vision Hiring Test 常见问题解答

我可以将多个技能结合在一起,为一个自定义评估吗?

是的,一点没错。自定义评估是根据您的职位描述进行的,并将包括有关您指定的所有必备技能的问题。

您是否有任何反交换或策略功能?

我们具有以下反交易功能:

  • 不可解决的问题
  • IP策略
  • Web Protoring
  • 网络摄像头Proctoring
  • 窃检测
  • 安全浏览器

阅读有关[Proctoring功能](https://www.adaface.com/proctoring)的更多信息。

如何解释考试成绩?

要记住的主要问题是评估是消除工具,而不是选择工具。优化了技能评估,以帮助您消除在技术上没有资格担任该角色的候选人,它没有进行优化以帮助您找到该角色的最佳候选人。因此,使用评估的理想方法是确定阈值分数(通常为55%,我们为您提供基准测试),并邀请所有在下一轮面试中得分高于门槛的候选人。

我可以使用该测试的经验水平?

每个ADAFACE评估都是为您的职位描述/理想候选角色定制的(我们的主题专家将从我们的10000多个问题的图书馆中选择正确的问题)。可以为任何经验级别定制此评估。

每个候选人都会得到同样的问题吗?

是的,这使您比较候选人变得容易得多。 MCQ问题的选项和问题顺序是随机的。我们有[抗欺骗/策略](https://www.adaface.com/proctoring)功能。在我们的企业计划中,我们还可以选择使用类似难度级别的问题创建多个版本的相同评估。

我是候选人。我可以尝试练习测试吗?

不,不幸的是,我们目前不支持实践测试。但是,您可以使用我们的[示例问题](https://www.adaface.com/questions)进行练习。

使用此测试的成本是多少?

您可以查看我们的[定价计划](https://www.adaface.com/pricing/)。

我可以免费试用吗?

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