A machine learning engineer is working with a financial dataset that contains multiple numerical features, including income, loan amount, and transaction frequency. Some features are normally distributed, while others have a highly skewed distribution with extreme outliers.
Which of the following approaches best ensures uniformity across features before training a model?

• A. Apply log transformation to skewed features before standardizing them with z-score normalization
• B. Scale all numerical features using min-max normalization
• C. Remove outliers before applying standardization
• D. Use one-hot encoding to transform numerical features into categorical representations

You are tasked with designing and implementing a benchmark to compare the performance of different deep learning frameworks, including TensorFlow, PyTorch, and JAX, using NVIDIA GPUs.
Which of the following is the most effective approach to ensure an accurate and fair comparison?

• A. Run each framework with default settings to compare their out-of-the-box performance without any optimizations.
• B. Compare training times only without considering throughput, power efficiency, or memory utilization.
• C. Ensure identical hardware configurations, dataset preprocessing, and model architectures while leveraging NVIDIA's Nsight Systems and DLProf for analysis.
• D. Use mixed precision (FP16) training only in TensorFlow to maximize performance while keeping other frameworks at FP32.

You have a massive time-series dataset containing millions of records per day, and you need to perform forecasting at scale.
Which of the following techniques best utilizes NVIDIA technologies to optimize time-series forecasting?

• A. Convert time-series data to tensors and train a deep learning model using PyTorch with mixed precision on an NVIDIA GPU.
• B. Run Facebook Prophet on a multi-core CPU environment to leverage parallel forecasting.
• C. Use RAPIDS cuML's XGBoost-GPU implementation to forecast time-series patterns with improved training efficiency.
• D. Use RAPIDS cuDF and Dask-cuDF to preprocess the data and train an LSTM model with PyTorch Lightning on multiple GPUs.

A data scientist is working on training a deep learning model in a cloud-based environment. The dataset is large, and model convergence is taking too long on a standard CPU instance.
To optimize performance through GPU acceleration, which of the following strategies should the data scientist implement?

• A. Store all training data in RAM and load it directly to the CPU for processing.
• B. Disable CUDA and use only OpenMP to parallelize computations across CPU cores.
• C. Use a cloud instance with multiple GPUs and enable mixed-precision training.
• D. Increase the number of CPU cores and distribute training across multiple CPU threads.

You are tasked with profiling a deep learning model using NVIDIA's DLProf to identify performance bottlenecks and optimize resource utilization.
Which of the following statements correctly describes the capabilities of DLProf?

• A. DLProf requires significant modifications to the source code to collect profiling data.
• B. DLProf only works with TensorFlow models and does not support PyTorch-based workloads.
• C. DLProf can generate detailed reports that highlight kernel-level execution times and GPU utilization trends.
• D. DLProf is primarily designed for debugging model accuracy rather than performance analysis.