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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

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.

これらは、10,000以上の質問のライブラリからのわずかなサンプルです。これに関する実際の質問 Machine Learning Assessment Test グーグルできません.

🧐 Question

Easy

Gradient descent optimization
Gradient Descent
Solve
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
Solve
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

Medium

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

Easy

Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering
Solve
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
Solve
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?
🧐 Question🔧 Skill

Easy

Gradient descent optimization
Gradient Descent
2 mins
Machine Learning
Solve

Medium

Less complex decision tree model
Model Complexity
Overfitting
2 mins
Machine Learning
Solve

Medium

n-gram generator
2 mins
Machine Learning
Solve

Easy

Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering
2 mins
Machine Learning
Solve

Easy

Sensitivity and Specificity
Confusion Matrix
Model Evaluation
2 mins
Machine Learning
Solve
🧐 Question🔧 Skill💪 Difficulty⌛ Time
Gradient descent optimization
Gradient Descent
Machine Learning
Easy2 mins
Solve
Less complex decision tree model
Model Complexity
Overfitting
Machine Learning
Medium2 mins
Solve
n-gram generator
Machine Learning
Medium2 mins
Solve
Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering
Machine Learning
Easy2 mins
Solve
Sensitivity and Specificity
Confusion Matrix
Model Evaluation
Machine Learning
Easy2 mins
Solve
Reason #4

1200+ customers in 75 countries

customers in 75 countries
Brandon

Adafaceを使用すると、初期スクリーニングプロセスを75%以上最適化することができ、採用マネージャーとタレント獲得チームの両方にとって貴重な時間を解放しました!


Brandon Lee, 人々の頭, 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.

invite candidates
Reason #7

Detailed scorecards & benchmarks

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


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
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採用マネージャーは、パネルのインタビュー中に尋ねた技術的な質問を通して、どの候補者がより良いスコアを持っているかを知ることができ、得点しなかった人と差別化することができたと感じました。彼らです 非常に満足しています 候補者の品質は、ADAFACEスクリーニングで最終候補になりました。


85%
スクリーニング時間の短縮

Machine Learning Online Test FAQs

機械学習テストにはどのような質問が含まれていますか?

この雇用前の機械学習テストは、候補者が自分の能力を実証する必要があるシナリオベースの質問で構成されています。

  • 機械学習アルゴリズムのデータを準備します
  • ロジスティック回帰、サポートベクトルマシン、決定ツリー、ランダムフォレストなどのMLアルゴリズムを分類するために使用する
  • クラスタリングアルゴリズムを作成します
  • 特定のユースケースに最も適切なアルゴリズムを提案する
  • 学習アルゴリズムのパフォーマンスを推定します

このテストまたは評価は、シニア機械学習エンジニアの役割に使用できますか?

シニア機械学習エンジニアの場合、カスタムテストをリクエストできます。 48時間以内に、当社の主題の専門家は、職務記述書と年功序列レベルに従って評価をカスタマイズします。ファンダメンタルズに加えて、上級の役割の典型的なテストは、候補者の能力のテストに焦点を当てます。

  • 構造MLプロジェクト
  • さまざまな機械学習アルゴリズムの欠点を特定します
  • データクリーニングとデータラベル付けプロセスを設計します
  • 適切な評価メトリックを選択して、モデルのパフォーマンスを向上させます
  • 機械学習アルゴリズムに対するハードウェアパフォーマンスの影響を評価する

複数のスキルを1つのカスタム評価に組み合わせることはできますか?

そのとおり。カスタム評価は、職務内容に基づいて設定され、指定したすべての必須スキルに関する質問が含まれます。

アンチチートまたは監督の機能はありますか?

次のアンチチート機能があります。

  • グーグル不可能な質問
  • IP監督
  • Webの提案
  • ウェブカメラの監督
  • 盗作の検出
  • 安全なブラウザ

[プロクチャリング機能](https://www.adaface.com/proctoring)の詳細をご覧ください。

テストスコアを解釈するにはどうすればよいですか?

留意すべき主なことは、評価が選択ツールではなく排除ツールであることです。スキル評価が最適化され、技術的にその役割の資格がない候補者を排除するのに役立ちます。これは、役割の最良の候補者を見つけるのに役立つために最適化されていません。したがって、評価を使用する理想的な方法は、しきい値スコア(通常は55%、ベンチマークを支援します)を決定し、インタビューの次のラウンドのしきい値を超えてスコアを上回るすべての候補者を招待することです。

このテストを使用できますか?

各ADAFACE評価は、職務記述書/理想的な候補者のペルソナにカスタマイズされます(当社の主題の専門家は、10000以上の質問のライブラリからあなたの評価に適切な質問を選択します)。この評価は、あらゆる経験レベルでカスタマイズできます。

すべての候補者は同じ質問を受け取りますか?

私は候補者です。練習テストを試すことはできますか?

いいえ。残念ながら、現時点では練習テストをサポートしていません。ただし、[サンプルの質問](https://www.adaface.com/questions)を使用するには、練習できます。

このテストを使用するコストはいくらですか?

無料トライアルを受けることはできますか?

私はちょうど有料プランに移りました。カスタム評価をリクエストするにはどうすればよいですか?

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今日、最も候補者のフレンドリーなスキル評価ツールをお試しください。
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40 min tests.
No trick questions.
Accurate shortlisting.
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