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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a large dataset containing 500 million records stored as a parquet file. Your task is to perform data filtering, aggregation, and transformation as efficiently as possible.
Which of the following approaches would be the best choice for accelerated data manipulation using NVIDIA technologies?

A) Use RAPIDS cuDF to load, filter, and transform the dataset on the GPU
B) Load the dataset into pandas and use apply() for transformations
C) Use multiprocessing in Python to parallelize pandas operations across CPU cores
D) Convert the dataset to a NumPy array and use standard Python loops

2. Which of the following Nvidia technologies is primarily used for performing benchmarking and optimizing GPU-accelerated deep learning workflows, especially focusing on model training performance?

A) Nvidia Triton Inference Server
B) Nvidia Nsight Systems
C) Nvidia DeepStream SDK
D) Nvidia CUDA

3. You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?

A) Convert the dataset to a NumPy array and manually replace missing values with the mean
B) Use fillna() with a fixed value on the GPU using cuDF
C) Apply a deep learning-based imputation model before moving data to the GPU
D) Drop all rows containing missing values using Pandas before transferring data to the GPU

4. You are training a machine learning model using RAPIDS cuML and need to ensure that all numeric features are standardized for better model performance.
Which of the following is the best approach for scaling data using RAPIDS?

A) scaler = cuml.preprocessing.StandardScaler()
B) df_scaled = (df - df.min()) / (df.max() - df.min())
C) df_scaled = scaler.fit_transform(df)
D) df_scaled = df / df.max()
E) df_scaled = df.apply(lambda x: x / np.linalg.norm(x))

5. A data scientist is analyzing sales data for an e-commerce company that experiences strong seasonal trends (e.g., increased sales during holiday seasons). The goal is to accurately forecast future sales using GPU-accelerated data science techniques.
Which approach would be the most effective?

A) Train a Decision Tree model using cuML to classify future sales trends.
B) Use a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and optimize it using NVIDIA RAPIDS.
C) Apply k-Means clustering to identify seasonal patterns and extrapolate future values.
D) Compute a rolling average and manually adjust for seasonality based on previous peak sales months.

Solutions: