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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A) Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
B) Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
C) Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
D) Train a generative adversarial network (GAN) using PyTorch and then use the generated samples in RAPIDS AI without any additional processing.

2. You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A) Load the data into a Spark DataFrame and visualize using Apache Zeppelin
B) Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib
C) Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly
D) Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively

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

4. You are working with a dataset containing billions of records stored in a Parquet file. You need to load this dataset efficiently into an NVIDIA-accelerated RAPIDS environment for feature engineering.
Which of the following is the best approach?

A) Use pandas.read_parquet() to load the dataset and then convert it to a cuDF DataFrame
B) Load the Parquet file into Dask and then convert it into a cuDF DataFrame for parallel processing
C) Load the Parquet file directly into a cuDF DataFrame using cudf.read_parquet()
D) Convert the dataset into a CSV format and use cudf.read_csv() to load it into RAPIDS

5. A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A) Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
B) Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
C) Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
D) Manually generate random values using Python's built-in random module and load them into a cuDF DataFrame.

Solutions: