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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are processing a dataset with billions of records and want to encode a categorical column efficiently using NVIDIA RAPIDS.
Which of the following methods correctly encodes categorical data using cuDF?

A) df['category_column'] = LabelEncoder().fit_transform(df['category_column'])
B) df['category_column'] = df['category_column'].one_hot_encode()
C) df['category_column'] = df['category_column'].astype('category')
D) df['category_column'] = df['category_column'].apply(lambda x: hash(x) % 1000)

2. You have developed a deep learning model using TensorFlow and trained it on an NVIDIA A100 GPU. The model is deployed in production and serves real-time inference requests. However, the inference latency is high, and you need to optimize performance without retraining the model.
Which of the following approaches is the most effective for optimizing inference performance using NVIDIA technologies?

A) Reduce the batch size to decrease computational overhead and improve latency.
B) Implement data augmentation techniques to improve inference efficiency.
C) Enable mixed precision training and retrain the model to improve inference speed.
D) Convert the model to ONNX format and use TensorRT for inference optimization.

3. 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) Use traditional CPU-based augmentation techniques like OpenCV to transform images and generate new data.
C) Use cuML.PCA() to reduce dimensionality and create synthetic samples by reconstructing the data with added noise.
D) Apply cuML.GaussianMixture() to generate new synthetic data points based on an estimated probability distribution.

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) Generate synthetic data by duplicating entries from the minority class using cudf.DataFrame.sample().
B) 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.
C) Use only the majority class data to train a model and generate synthetic data using a GAN (Generative Adversarial Network) in the RAPIDS ecosystem.
D) Create synthetic data by applying random transformations to the minority class, such as scaling, rotation, or flipping, using cuDF.

5. You have trained a machine learning model using cuML as part of the Modeling phase in the CRISP- DM framework. Now, you need to assess how well the model performs before moving forward with deployment.
Which of the following steps aligns best with the Evaluation phase of CRISP-DM using NVIDIA technologies?

A) Deploy the model to an edge device using TensorRT for real-time inference.
B) Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().
C) Define the problem statement and collect relevant datasets before training the model.
D) Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.

Solutions: