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Machine Learning Assessment Test

About the test:

The pre-employment machine learning assessment test evaluates a candidate's understanding of machine learning fundamentals like feature engineering, regression, variance, conditional probability, clustering, decision trees, nearest neighbors, Naïve Bayes, bias and overfitting. The test also assesses them on their ability to collect and prepare the dataset, train a model, evaluate the model, and iteratively improve the model's performance.

Covered skills:

  • Linear Regression
  • Classification
  • Gradient Descent
  • Accuracy Matrix
See all covered skills

9 reasons why
9 reasons why

Adaface Machine Learning Assessment Test is the most accurate way to shortlist Machine Learning Engineers



Reason #1

Tests for on-the-job skills

The Machine Learning Assessment 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.

The pre-employment machine learning assessment test screens candidates for the typical skills recruiters look for Machine Learning developer roles:

  • Experience designing, developing, and researching Machine Learning models
  • Deep understanding of fundamental data structures and data modeling
  • Expertise in math, probability and statistics
  • Ability to write robust code
  • Experience with machine learning frameworks (like Keras, PyTorch, Tensorflow etc.) and libraries (like scikit-learn, Numpy, Pandas etc.)
  • Ability to choose hardware for running an ML model with the needed latency

The insights generated from this assessment can be used by recruiters and hiring managers to identify the best candidates for Machine Learning developer roles. Anti-cheating features enable you to be comfortable with conducting assessments online. The Machine Learning Assessment Test is ideal for helping recruiters identify which candidates have the skills to do well on the job.

Reason #2

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.

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 ->
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.

These are just a small sample from our library of 10,000+ questions. The actual questions on this Machine Learning Assessment Test will be non-googleable.

🧐 Question

Hard

n-gram generator
Solve
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
🧐 Question🔧 Skill

Hard

n-gram generator
2 mins
Machine Learning
Solve
🧐 Question🔧 Skill💪 Difficulty⌛ Time
n-gram generator
Machine Learning
Hard2 mins
Solve
Reason #4

1200+ customers in 75 countries

Brandon

With Adaface, we were able to optimise our initial screening process by upwards of 75%, freeing up precious time for both hiring managers and our talent acquisition team alike!


Brandon Lee, Head of People, Love, Bonito

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Machine Learning Assessment Test 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.

Reason #6

1 click candidate invites

Email invites: You can send candidates an email invite to the Machine Learning Assessment Test 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.

Reason #7

Detailed scorecards & comparative results

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.

Reason #9

Advanced Proctoring


About Machine Learning Developer Roles

Machine learning (ML) is a subset of artificial intelligence (AI) that enables software programmes to grow increasingly effective at predicting outcomes without explicitly programming them to do so. Machine learning algorithms estimate new output values by using past data as input.

A machine learning (ML) developer is an expert in training models with data. Following that, the models are utilised to automate activities such as image classification, speech recognition, and market forecasting.

A machine learning developer creates a solution that is unique to each situation. The only way to get the best results is to thoroughly process the data and use the appropriate algorithm for the current situation.

Typical Machine Learning Developer/Engineer responsibilities include:

  • Understanding corporate objectives and creating models to assist them be accomplished, as well as measurements to track their success
  • Managing available resources such as hardware, data, and staff in order to meet deadlines
  • Analyzing the ML algorithms that may be utilised to tackle a particular problem and evaluating them based on their likelihood of success
  • Exploring and visualising data
  • Verifying and/or assuring data quality through data cleansing
  • Locating public datasets that might be utilised for training
  • Developing Validation Methodologies
  • Specifying the preprocessing or feature engineering that will be performed on a given dataset
  • Creating pipelines for data augmentation
  • Analyzing the model's flaws and devising solutions to overcome them

What roles can I use the Machine Learning Assessment Test for?

  • Machine Learning Engineer
  • Machine Learning Developer

What topics are covered in the Machine Learning Assessment Test?

Training Data & Test Data
Bias-Variance Tradeoff
Overfitting and Underfitting
Classification
Statistics
Feature Engineering
Probability
Data Preparation
Regularization
Cross-Validation
Uni-variate Analysis
Bi-variate Analysis
Multivariate Analysis
Inferential Statistical Analysis
Outliers
Scaling (Standardization, Normalization)
Pre-Model Building
Model Splitting
Supervised Learning Algorithms
Regression: Linear Regression, Logistic Regression
Decision Tree
K-Nearest Neighbors
Naive Bayes
Support Vector Machines
Unsupervised Learning Algorithms
Segmentation
K-Means Clustering
Agglomerative Hierarchical Clustering
Dimensionality Reduction
Principal Component Analysis
Model Validation
Hyper Parameter Tuning
Singapore government logo

The hiring managers felt that through the technical questions that they asked during the panel interviews, they were able to tell which candidates had better scores, and differentiated with those who did not score as well. They are highly satisfied with the quality of candidates shortlisted with the Adaface screening.


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Machine Learning Online Test FAQs

机器学习测试包括什么类型的问题?

这项就业前机器学习测试包括基于方案的问题,这些问题要求候选人证明其能力:

  • 准备机器学习算法的数据
  • 使用ML算法,例如逻辑回归,支持向量机,决策树和随机森林进行分类
  • 构建聚类算法
  • 为特定用例提出最合适的算法
  • 估计学习算法的性能

该测试或评估可以用于高级机器学习工程师角色吗?

对于高级机器学习工程师,您可以请求自定义测试。在48小时内,我们的主题专家将根据您的职位描述和资历级别自定义评估。除基本面外,该测试还将重点介绍候选人的能力:

  • 结构ML项目
  • 确定各种机器学习算法的缺点
  • 设计数据清洁和数据标记过程
  • 选择适当的评估指标以提高模型性能
  • 评估硬件性能对机器学习算法的影响

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?

我刚刚搬到了一个付费计划。我如何要求自定义评估?

Join 1200+ companies in 75+ countries.
立即尝试最候选的友好技能评估工具。
Ready to use the Adaface Machine Learning Assessment Test?
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40 min tests.
No trick questions.
Accurate shortlisting.
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