Why you should use Computer Vision Test?

The Test della visione artificiale 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: Esperienza nello sviluppo di applicazioni di visione artificiale Forte conoscenza degli algoritmi di apprendimento automatico Competenza nel linguaggio di programmazione Python Competente nell'uso di framework di visione artificiale Capacità di eseguire compiti di riconoscimento delle immagini Comprensione delle tecniche di rilevamento degli oggetti Conoscenza dei metodi di segmentazione delle immagini Esperienza nell'estrazione delle caratteristiche dalle immagini Familiarità con reti neurali convoluzionali Competenza nelle architetture di rete neurale

Computer Vision Test

About the test:

Il test della visione informatica valuta la conoscenza e la comprensione di un candidato delle tecniche di visione artificiale, compresi gli algoritmi di apprendimento profondo e di apprendimento automatico. Valuta le abilità nel riconoscimento delle immagini, nel rilevamento degli oggetti, nella segmentazione delle immagini e nell'estrazione delle caratteristiche.

Covered skills:

Riconoscimento dell'immagine
Segmentazione delle immagini
Reti neurali convoluzionali
Classificazione delle immagini
Apprendimento automatico
Framework CV

Rilevamento di oggetti
Estrazione delle caratteristiche
Reti neurali
Apprendimento approfondito
Pitone

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9 reasons why

Adaface Computer Vision Assessment Test is the most accurate way to shortlist Ingegnere della visione artificiales

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:

Capacità di eseguire compiti di riconoscimento delle immagini
Capacità di rilevare oggetti nelle immagini
Capacità di segmentare accuratamente le immagini
Capacità di estrarre le funzioni dalle immagini
Capacità di lavorare con reti neurali convoluzionali (CNN)
Capacità di costruire reti neurali per compiti di visione artificiale
Capacità di classificare le immagini utilizzando tecniche di apprendimento automatico
Capacità di applicare i principi di apprendimento profondo alla visione informatica
Capacità di applicare algoritmi di apprendimento automatico ai problemi di visione artificiale
Capacità di codificare in Python per le attività di visione artificiale
Conoscenza di vari quadri di visione artificiale

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.

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

Questi sono solo un piccolo campione della nostra biblioteca di oltre 10.000 domande. Le domande reali su questo Test della visione artificiale sarà non googleabile.

🧐 Question
Medium Changed decision boundary	Try practice test
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	Try practice test
You are fine-tuning a Convolutional Neural Network (CNN) for image classification. The network architecture is as follows: 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	Try practice test
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	Try practice test
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: 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	Try practice test
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: 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	Try practice test
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?
Easy Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	Try practice test
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	Try practice test
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: Based on the confusion matrix, what are the sensitivity and specificity of the model?
Medium ZeroDivisionError and IndexError Exceptions	Try practice test
What will the following Python code output?
Medium Session File Handling Dictionary	Try practice test
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?
Medium Max Code Arrays	Try practice test
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:
Medium Recursive Function Recursion Dictionary Lists	Try practice test
Consider the following Python code: 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	Try practice test
What does the below function ‘fun’ does? 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	Try practice test
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	3 mins Deep Learning	Try practice test
You are fine-tuning a Convolutional Neural Network (CNN) for image classification. The network architecture is as follows: 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	3 mins Deep Learning	Try practice test
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	2 mins Machine Learning	Try practice test
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: 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	2 mins Machine Learning	Try practice test
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: 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	2 mins Machine Learning	Try practice test
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?
	Easy Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	2 mins Machine Learning	Try practice test
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	2 mins Machine Learning	Try practice test
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: Based on the confusion matrix, what are the sensitivity and specificity of the model?
	Medium ZeroDivisionError and IndexError Exceptions	2 mins Python	Try practice test
What will the following Python code output?
	Medium Session File Handling Dictionary	2 mins Python	Try practice test
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?
	Medium Max Code Arrays	2 mins Python	Try practice test
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:
	Medium Recursive Function Recursion Dictionary Lists	3 mins Python	Try practice test
Consider the following Python code: 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	4 mins Python	Try practice test
What does the below function ‘fun’ does? 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	💪 Difficulty	⌛ Time
	Changed decision boundary	Deep Learning	Medium	2 mins	Try practice test
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
	CNN Architecture Tuning Convolutional Neural Networks Hyperparameter Optimization	Deep Learning	Medium	3 mins	Try practice test
You are fine-tuning a Convolutional Neural Network (CNN) for image classification. The network architecture is as follows: 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
	CNN for Imbalanced Image Dataset Convolutional Neural Networks Imbalanced Datasets	Deep Learning	Medium	3 mins	Try practice test
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
	Gradient descent optimization Gradient Descent	Machine Learning	Easy	2 mins	Try practice test
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: 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
	Less complex decision tree model Model Complexity Overfitting	Machine Learning	Medium	2 mins	Try practice test
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: 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
	n-gram generator	Machine Learning	Easy	2 mins	Try practice test
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?
	Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	Machine Learning	Easy	2 mins	Try practice test
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?
	Sensitivity and Specificity Confusion Matrix Model Evaluation	Machine Learning	Easy	2 mins	Try practice test
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: Based on the confusion matrix, what are the sensitivity and specificity of the model?
	ZeroDivisionError and IndexError Exceptions	Python	Medium	2 mins	Try practice test
What will the following Python code output?
	Session File Handling Dictionary	Python	Medium	2 mins	Try practice test
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?
	Max Code Arrays	Python	Medium	2 mins	Try practice test
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:
	Recursive Function Recursion Dictionary Lists	Python	Medium	3 mins	Try practice test
Consider the following Python code: 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?
	Stacking problem Stack Linkedlist	Python	Medium	4 mins	Try practice test
What does the below function ‘fun’ does? 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.

Reason #4

1200+ customers in 75 countries

Con Adaface siamo stati in grado di ottimizzare il nostro processo di screening iniziale fino al 75%, liberando tempo prezioso sia per i responsabili delle assunzioni che per il nostro team di acquisizione dei talenti!

Brandon Lee, Capo del Popolo, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Test della visione artificiale 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 Test della visione artificiale 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 & benchmarks

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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.

Reason #9

Advanced Proctoring

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About the Computer Vision Online Test

Why you should use Pre-employment Computer Vision Test?

The Test della visione artificiale 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:

Esperienza nello sviluppo di applicazioni di visione artificiale
Forte conoscenza degli algoritmi di apprendimento automatico
Competenza nel linguaggio di programmazione Python
Competente nell'uso di framework di visione artificiale
Capacità di eseguire compiti di riconoscimento delle immagini
Comprensione delle tecniche di rilevamento degli oggetti
Conoscenza dei metodi di segmentazione delle immagini
Esperienza nell'estrazione delle caratteristiche dalle immagini
Familiarità con reti neurali convoluzionali
Competenza nelle architetture di rete neurale

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?

Riconoscimento delle immagini
Il riconoscimento delle immagini è la capacità di un sistema informatico di identificare e classificare oggetti o motivi nelle immagini digitali. Questa abilità viene misurata nel test per valutare la comprensione da parte di un candidato dei concetti e delle tecniche fondamentali utilizzate nel riconoscimento delle immagini, che è cruciale per vari compiti come l'identificazione degli oggetti, la ricerca di immagini e l'analisi del contenuto.
Rilevamento di oggetti
Il rilevamento di oggetti è un'attività di visione artificiale che prevede la ricerca e la localizzazione di oggetti in immagini o video. Questa abilità viene misurata nel test per valutare la conoscenza di un candidato di algoritmi e metodi utilizzati per rilevare e individuare oggetti, che è essenziale in applicazioni come la sorveglianza, i veicoli autonomi e i sistemi di ricerca basati su immagini.
Segmentazione delle immagini.
La segmentazione delle immagini è il processo di partizionamento di un'immagine in più regioni o segmenti, con l'obiettivo di semplificare o analizzare la rappresentazione dell'immagine. La misurazione di questa abilità nel test consente ai reclutatori di valutare la capacità di un candidato di utilizzare tecniche e algoritmi per la segmentazione delle immagini, che svolge un ruolo cruciale in applicazioni come l'analisi delle immagini mediche, il riconoscimento degli oggetti e l'editing delle immagini. </h4> <p> L'estrazione delle caratteristiche comporta la derivazione di informazioni o funzionalità significative da dati grezzi, come immagini, per facilitare l'analisi o la classificazione successive. Questa abilità viene misurata nel test per valutare la comprensione da parte di un candidato delle tecniche di estrazione delle caratteristiche utilizzate nella visione artificiale, che sono cruciali per compiti come il riconoscimento degli oggetti, la corrispondenza delle immagini e l'analisi dei pattern.
Network neurali convoluzionali </H4 > <p> Reti neurali convoluzionali (CNN) sono modelli di apprendimento profondo specificamente progettati per l'elaborazione di dati visivi, come le immagini. Questa abilità è misurata nel test per valutare la conoscenza di un candidato delle architetture della CNN, nonché la loro capacità di addestrare e applicare CNN per compiti come la classificazione delle immagini, il rilevamento degli oggetti e la segmentazione delle immagini. </p> <h4> Networks neurali </// H4> <p> Le reti neurali sono modelli computazionali ispirati alla struttura e alla funzione del cervello umano, utilizzati per il riconoscimento dei pattern e le attività di apprendimento automatico. La misurazione di questa abilità nel test consente ai reclutatori di valutare la comprensione da parte di un candidato dei concetti di rete neurale e la loro capacità di applicare reti neurali per risolvere i problemi di visione informatica. compito di assegnare un'etichetta o una categoria a un'immagine in base al suo contenuto. Questa abilità viene misurata nel test per valutare la conoscenza di un candidato sugli algoritmi e le tecniche di classificazione applicate alle immagini, essenziali per varie applicazioni come la ricerca di immagini, il filtro del contenuto e il tagging di immagini automatizzato. </p> <h4> Deep Learning </spedment H4> <p> Deep Learning è un sottocampo di apprendimento automatico che si concentra sulla costruzione e la formazione di reti neurali artificiali con più livelli. La misurazione di questa abilità nel test consente ai reclutatori di valutare la comprensione da parte di un candidato dei principi di apprendimento profondo e la loro capacità di applicare modelli di apprendimento profondo a compiti come il riconoscimento delle immagini, il rilevamento degli oggetti e la generazione di immagini. </p> <h4> Machine Learning </H4 > <p> Machine Learning è un ramo dell'intelligenza artificiale che si concentra sullo sviluppo di algoritmi e modelli in grado di apprendere e prendere previsioni o decisioni basate sui dati. Questa abilità viene misurata nel test per valutare la comprensione da parte di un candidato dei concetti di apprendimento automatico e la loro capacità di applicare le tecniche di apprendimento automatico ai problemi di visione artificiale. </p> <h4> Python
Python è un linguaggio di programmazione popolare Ampiamente utilizzato nel campo della visione artificiale e dell'apprendimento automatico. La misurazione di questa abilità nel test consente ai reclutatori di valutare la competenza di un candidato nella programmazione di Python, nonché la loro capacità di implementare algoritmi e modelli di visione artificiale usando i framework CV
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 Test della visione artificiale to be based on.
Riconoscimento dell'immagine
Rilevamento di oggetti
Segmentazione delle immagini
Estrazione delle caratteristiche
Reti neurali convoluzionali (CNN)
Reti neurali
Classificazione delle immagini
Apprendimento approfondito
Apprendimento automatico
Pitone
Framework CV
Preelaborazione
Funzioni di attivazione
Funzioni di perdita
Algoritmi di ottimizzazione
Aumento dei dati
Trasferisci l'apprendimento
Backpropagation
Regolarizzazione
Tuning iperparametro
Convalida incrociata
Classificazione binaria
Classificazione multi-classe
Localizzazione degli oggetti
Regressione della scatola di delimitazione
Segmentazione di istanza
Segmentazione semantica
Architettura encoder-decoder
Reti neurali ricorrenti (RNN)
Strati convoluzionali
Raggruppare strati
Livelli completamente collegati
Normalizzazione batch
Ritirarsi
Preelaborazione dell'immagine
Tecniche di aumento dei dati
Etichettatura dei dati
Selezione delle caratteristiche
Analisi dei componenti principali (PCA)
Regressione lineare
Regressione logistica
Support Vector Machines (SVM)
Foreste casuali
K-Nearest Neighbours (KNN)
Naive Bayes
Metriche di valutazione del modello
Matrix di confusione
Precisione e richiamo
Punteggio F1
Curva caratteristica operativa del ricevitore (ROC)
Punteggio AUC-ROC
Ricerca della griglia
Convalida incrociata-folle
Apprendimento di ensemble
Eccessivamente idoneo e sottofitto
Sintassi di Python
Tipi variabili
Flusso di controllo
Funzioni
Gestione dei file
Biblioteche Python
Numpy
Panda
Matplotlib

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What roles can I use the Computer Vision Test for?

Ingegnere della visione artificiale
Ingegnere dell'apprendimento automatico
Ricercatore di intelligenza artificiale
Data scienziata
Sviluppatore di software
Analista dati
Ingegnere di elaborazione delle immagini
Ricercatore
Consulente per la visione artificiale
Ingegnere dei dati

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

Capacità nella classificazione delle immagini
Profonda comprensione dei concetti di apprendimento profondo
Competenza negli algoritmi di apprendimento automatico
Competente nel linguaggio di programmazione Python
Capacità di funzionare con i quadri della visione artificiale
Esperienza nell'implementazione di modelli di riconoscimento delle immagini
Forte comprensione dei principi di rilevamento degli oggetti
Conoscenza di tecniche di segmentazione delle immagini avanzate
Competenza nei metodi di estrazione delle caratteristiche
Comprensione approfondita delle reti neurali convoluzionali

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Visualizza la scorecard campione

I responsabili delle assunzioni hanno ritenuto che, attraverso le domande tecniche poste durante le interviste del panel, erano in grado di individuare quali candidati avevano ottenuto i punteggi migliori e di differenziarli da quelli che non avevano ottenuto altrettanto punteggio. Sono altamente soddisfatto con la qualità dei candidati selezionati con lo screening Adaface.

85%

Riduzione del tempo di screening

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Computer Vision Hiring Test Domande frequenti

Posso combinare più competenze in una valutazione personalizzata?

Si assolutamente. Le valutazioni personalizzate sono impostate in base alla descrizione del tuo lavoro e includeranno domande su tutte le competenze indispensabili che specificate.

Hai in atto delle caratteristiche anti-cheat o procuratore?

Abbiamo in atto le seguenti caratteristiche anti-cheat:

Domande non googiche
Proctoring IP
procuratore web
Proctor di webcam
Rilevamento del plagio
Sicuro browser

Leggi di più sulle caratteristiche di procuratore.

Come interpreto i punteggi dei test?

La cosa principale da tenere a mente è che una valutazione è uno strumento di eliminazione, non uno strumento di selezione. Una valutazione delle competenze è ottimizzata per aiutarti a eliminare i candidati che non sono tecnicamente qualificati per il ruolo, non è ottimizzato per aiutarti a trovare il miglior candidato per il ruolo. Quindi il modo ideale per utilizzare una valutazione è decidere un punteggio di soglia (in genere il 55%, ti aiutiamo a benchmark) e invitiamo tutti i candidati che segnano al di sopra della soglia per i prossimi round di intervista.

Per quale livello di esperienza posso usare questo test?

Ogni valutazione di Adaface è personalizzata per la descrizione del tuo lavoro/ personaggio del candidato ideale (i nostri esperti in materia sceglieranno le domande giuste per la tua valutazione dalla nostra biblioteca di oltre 10000 domande). Questa valutazione può essere personalizzata per qualsiasi livello di esperienza.

Ogni candidato riceve le stesse domande?

Sì, ti rende molto più facile confrontare i candidati. Le opzioni per le domande MCQ e l'ordine delle domande sono randomizzate. Abbiamo anti-cheatri/procuratore in atto. Nel nostro piano aziendale, abbiamo anche la possibilità di creare più versioni della stessa valutazione con questioni di difficoltà simili.