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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A machine learning engineer is working on an image classification problem where the dataset is small and lacks variability. To improve generalization, the engineer decides to augment the dataset using NVIDIA RAPIDS.
What is the best method to generate synthetic data efficiently while leveraging GPU acceleration?

A) Use cuDF with cudf.DataFrame.sample() to create new samples by randomly selecting existing rows.
B) Apply cuML.GaussianMixture() to generate new synthetic data points based on an estimated probability distribution.
C) Use cuML.PCA() to reduce dimensionality and create synthetic samples by reconstructing the data with added noise.
D) Use traditional CPU-based augmentation techniques like OpenCV to transform images and generate new data.

2. You are processing a large dataset using NVIDIA Dask-cuDF to distribute GPU-accelerated computation across multiple nodes. Users report inconsistent execution times, with some jobs taking significantly longer than expected.
Which of the following actions would best help diagnose the performance bottleneck?

A) Switch to using Pandas with Dask to compare execution speed differences.
B) Use Dask's dashboard and NVTX markers to analyze task execution times and GPU utilization.
C) Reduce the number of Dask workers to minimize parallel execution overhead.
D) Limit GPU memory usage to force more frequent spilling to disk and observe performance differences.

3. A data scientist is working with a large dataset containing millions of records and aims to accelerate the data preprocessing workflow using NVIDIA technologies.
Which of the following approaches is the most effective for optimizing data preprocessing performance using GPUs?

A) Using RAPIDS cuDF to replace pandas operations
B) Running data transformations in a multi-threaded CPU environment
C) Using NumPy for array computations and parallelizing with Python's multiprocessing
D) Using Dask to distribute processing across multiple CPU cores

4. You need to generate synthetic data to augment an imbalanced dataset using RAPIDS™ and cuDF.
Which of the following strategies would be most effective in producing high-quality synthetic data for the minority class?

A) Use only the majority class data to train a model and generate synthetic data using a GAN (Generative Adversarial Network) in the RAPIDS ecosystem.
B) Generate synthetic data by duplicating entries from the minority class using cudf.DataFrame.sample().
C) Use synthetic data generation libraries like SDV (Synthetic Data Vault) in conjunction with cuDF to create synthetic data that mimics the distribution of the minority class.
D) Create synthetic data by applying random transformations to the minority class, such as scaling, rotation, or flipping, using cuDF.

5. You are working on optimizing a deep learning model for inference on an NVIDIA GPU. You decide to use NVIDIA DLProf to profile the model and analyze its performance. After running DLProf, you review the generated reports and find that the GPU Utilization is significantly lower than expected.
Which of the following is the most likely reason for this issue, as indicated by the profiling data?

A) The GPU lacks sufficient VRAM, causing frequent memory swaps to system RAM.
B) The batch size is too large, leading to excessive memory allocation failures.
C) The model contains a large number of small, inefficient kernel launches that introduce overhead.
D) DLProf detected a high level of tensor core utilization, which generally indicates poor performance.

Solutions: