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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually configuring each system.
Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?

A) Running the model in a local Python virtual environment and copying dependencies manually
B) Compiling all dependencies into the host machine and using system-wide installations
C) Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies
D) Using Docker without GPU support and relying on CPU fallback when running TensorFlow

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

3. You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?

A) NVIDIA Nsight Systems for managing dataset storage and retrieval performance.
B) NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.
C) NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.
D) NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.

4. You are working with a large dataset in RAPIDS cuDF and plan to standardize the numerical features using cuml.preprocessing.StandardScaler(). However, some columns contain missing values.
What is the best approach to handle the missing values before applying standardization?

A) Use cudf.DataFrame.fillna(method='ffill') to forward-fill missing values.
B) Use cuml.impute.SimpleImputer(strategy='mean') to replace missing values with the column mean.
C) cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.
D) Replace missing values with zero before standardization.

5. Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?

A) Modify the training dataset during model execution
B) Automatically adjust the learning rate based on the model's convergence
C) Visualize GPU memory utilization over time
D) Increase batch size to improve accuracy

Solutions: