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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A financial services company is using NVIDIA RAPIDS cuML to train a credit risk assessment model.
The dataset contains hundreds of numerical and categorical features, including loan amount, credit score, income, employment history, and previous loan defaults.
To optimize feature selection using NVIDIA technologies, which approach should they take?

A) Use cuML's feature selection algorithms, such as Recursive Feature Elimination (RFE), to identify the most important predictors.
B) Avoid feature selection, as modern deep learning models are capable of automatically handling redundant and irrelevant features.
C) Keep all features in the dataset to ensure the model captures as much information as possible, even if some features are redundant.
D) Convert all numerical features into categorical variables to simplify the data and reduce the need for feature selection.

2. In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A) Lower cost of cloud infrastructure
B) Better for memory-intensive workloads
C) Faster parallel processing for large datasets
D) Lower energy consumption compared to CPUs
E) Efficient handling of matrix operations in machine learning models

3. A data scientist is working with an imbalanced dataset in a fraud detection project. The dataset contains 1 million transactions, but only 2% of them are labeled as fraudulent. To improve the performance of the model, the scientist decides to generate synthetic data using NVIDIA RAPIDS cuDF.
Which of the following approaches is the best way to generate synthetic samples while preserving data characteristics?

A) Use cudf.DataFrame.sample(frac=0.5, replace=True) to oversample the minority class.
B) Apply cuML.SMOTE() to generate synthetic samples based on the minority class distribution.
C) Use cudf.DataFrame.interpolate(method='linear') to create new fraudulent samples by interpolating between existing ones.
D) Use cudf.DataFrame.append(cudf.DataFrame.random()) to create new fraudulent transactions.

4. A data scientist is working with a large dataset that contains string-based numeric values that need to be converted to floating-point numbers for further analysis. The dataset is stored as a cuDF DataFrame, and the scientist needs to ensure the conversion is performed optimally on a GPU.
Which of the following is the best method for converting string-based numeric values to floating-point numbers using NVIDIA-accelerated processing?

A) Convert the cuDF DataFrame to a Pandas DataFrame first, then apply astype(float) and convert it back to cuDF.
B) Use NumPy's astype(float) method after converting the cuDF DataFrame into a NumPy array.
C) Use pandas.to_numeric() since pandas automatically handles type conversion.
D) Use cudf.DataFrame.astype(float) to convert string values to floating-point numbers efficiently on a GPU.

5. You are setting up a deep learning model for training on a multi-GPU cluster. You want to maximize training efficiency while maintaining model convergence.
Which of the following strategies is most effective in ensuring efficient multi-GPU training?

A) Reduce batch size to ensure each GPU receives only a small portion of the training data.
B) Use Model Parallelism, where different layers of the model are placed on different GPUs to reduce communication overhead.
C) Train each GPU independently on a different dataset to reduce the communication bottleneck.
D) Use Data Parallelism, where each GPU gets a different portion of the dataset, but gradients are averaged across all GPUs.

Solutions: