NVIDIA Deep Learning Institute: Fundamentals of Deep Learning Practice Test
Build your confidence for NVIDIA Deep Learning Institute: Fundamentals of Deep Learning. Practice the concepts, understand the answers, and strengthen your knowledge one question at a time.
Try a sample questionExam overview and details
The NVIDIA Deep Learning Institute: Fundamentals of Deep Learning Practice Test is a comprehensive assessment designed to validate foundational and intermediate knowledge of deep learning concepts, tools, and workflows. It covers five core domains: Fundamentals, Core Knowledge, Best Practices, Practical Application, and Advanced Topics. The exam tests a learner's ability to understand neural network architectures, training dynamics, optimization techniques, and deployment considerations. It is ideal for data scientists, machine learning engineers, software developers, and students who have completed introductory deep learning coursework or have equivalent hands-on experience. By passing this practice test, learners will gain confidence in their ability to design, train, and evaluate deep learning models using industry-standard frameworks such as TensorFlow and PyTorch. The exam emphasizes both theoretical understanding and practical problem-solving, including data preprocessing, model tuning, and performance analysis. It serves as a benchmark for readiness before pursuing official NVIDIA DLI certifications or applying deep learning skills in real-world projects. The content is aligned with NVIDIA's curriculum, ensuring relevance to current industry practices.
Sample Questions
Choose an answer and explore the explanation to see how practice works.
An engineer exports a trained ResNet-50 to ONNX, then uses the TensorRT Python API on an A10 GPU to build an engine with FP16 enabled and INT8 calibration using a calibration dataset of 800 representative images. After building, the engine achieves 3.8x higher throughput than the original PyTorch FP32 model at batch size 8, with top-1 accuracy dropping only 0.6 points. Which statement correctly explains both the speedup and the small accuracy change?
An NLP engineer trains a unidirectional LSTM language model on 50-token sequences. After convergence, the team adds a second LSTM layer on top of the first (stacked) and also wraps both with dropout=0.25 between time steps and between layers. They observe that the validation perplexity improves by 4 points relative to the single-layer baseline, but training perplexity is now higher than validation perplexity for the first 15 epochs. What is the most accurate interpretation?
An ML engineer is implementing a CNN for 32x32 RGB CIFAR-10 images. The first layer is a 3x3 convolution with 32 filters, stride=1, padding=1, followed by ReLU and 2x2 max-pooling with stride=2. When the input resolution is doubled to 64x64 while keeping every other layer identical, what is the spatial size of the feature map immediately after the first pooling layer?
A researcher trains a two-hidden-layer MLP on the Iris dataset using stochastic gradient descent with a learning rate of 0.01. The model reaches 98% training accuracy but the decision boundaries appear overly smooth and fail to capture the non-linear separation between two of the classes in a held-out visualization. Which modification to the network architecture would most directly increase its capacity to model more complex decision boundaries?
A developer builds two TensorRT engines from the identical ONNX graph of a U-Net segmentation model. Engine A uses FP16 with no INT8. Engine B additionally enables INT8 with a calibration dataset of 1200 images and sets the builder flag for INT8. On an Orin AGX, Engine B runs 1.9x faster than Engine A but produces visibly blocky artifacts on fine structures (thin lines, small text). Which statement best explains the artifacts?
Exam insights and study advice
Deep learning is transforming industries from healthcare and autonomous vehicles to finance and natural language processing. This practice test matters because it bridges the gap between theoretical knowledge and practical competence. By mastering the topics covered, you will be better equipped to build models that generalize well, avoid common pitfalls like overfitting or vanishing gradients, and deploy solutions that deliver measurable business impact. The exam's focus on best practices ensures you learn not just how to build a model, but how to build one that is robust, scalable, and maintainable. Real-world impact includes faster time-to-production for AI solutions, reduced computational costs through efficient training, and improved model accuracy that directly affects user experience and decision-making. Whether you are preparing for a certification or seeking to validate your skills for a job role, this practice test provides a clear, actionable roadmap for professional growth.
Recommended
These are the backgrounds the certifying body suggests. Check the vendor's own page for anything it formally requires.