Deep Learning Test
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About the test
The Deep Learning test evaluates knowledge of designing, training, and deploying neural network–based models to solve complex pattern recognition problems across structured data, images, video, and text.
The assessment features work-sample tasks such as:
- Tuning hyperparameters to optimize model performance.
- Diagnosing and mitigating issues such as overfitting and underfitting.
- Evaluating and improving YOLO-based object detection.
A good deep learning practitioner understands how architectural choices, optimization strategy, and data quality interact to drive performance, and can translate model outputs into reliable, actionable conclusions while maintaining strong generalization to unseen data.
Sample public questions
You deployed a convolutional neural network (CNN) that detects defective products from factory line images.
The confusion matrices below show the results on the test set and on production data collected after deployment:
Which of the following steps would be the most appropriate to improve performance on production data?
You’ve fine-tuned a BERT-based model to automatically categorize incoming support tickets into 5 predefined issue types. The model is trained using the subject and body text of the ticket. Below are the performance metrics of the model in each class:
Which of the following actions would help to improve the model’s performance?
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Skills and topics tested
- Deep Learning
- Object Detection
- Video
- Model Settings
- Overfitting
For job roles
- Data Scientist
- Machine Learning Specialist
Sample candidate report
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Simple, straight-forward technical testing
TestDome is simple, provides a reasonable (though not extensive) battery of tests to choose from, and doesn't take the candidate an inordinate amount of time. It also simulates working pressure with the time limits.
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