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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You have a large-scale dataset consisting of IoT sensor readings collected at one-minute intervals across multiple locations. The dataset contains missing values and requires scaling before applying a machine learning model. You plan to use NVIDIA RAPIDS to preprocess and analyze the time-series data efficiently on GPUs.
Which of the following preprocessing steps is the most efficient approach using NVIDIA RAPIDS?

A) Use pandas for missing value imputation, then normalize the data using NumPy before converting to cuDF.
B) Use pandas to fill missing values and scale the data, then convert it to cuDF for training.
C) Use Dask for distributed missing value imputation and train a model using TensorFlow's CPU-based estimator.
D) Use cuDF to handle missing values with GPU-accelerated interpolation and apply cuML's StandardScaler for feature scaling.

2. You are analyzing a large financial dataset containing stock market tick-by-tick data stored in a cuDF DataFrame. Since the dataset contains billions of data points, you need to aggregate it at the minute level before visualizing price trends efficiently.
Which of the following is the best approach for aggregating and visualizing this time-series data using NVIDIA technologies?

A) Use cuDF's .groupby() function to aggregate at the minute level, then visualize using hvPlot
B) Use cuML's TSNE function to reduce dimensionality before visualizing with Bokeh
C) Convert cuDF to Pandas, aggregate using .resample() in Pandas, and visualize using Matplotlib
D) Load the data into a relational database (e.g., PostgreSQL), run an SQL query for aggregation, and visualize using Seaborn

3. You are working with a dataset containing 2 billion rows of financial transactions, and you need to perform exploratory data analysis (EDA) before building a predictive model.
Which of the following approaches is the most appropriate for handling this data efficiently?

A) Use SQLite to store the data locally and run queries sequentially to minimize memory consumption.
B) Convert the dataset to JSON format and use Python's built-in json module to parse and analyze it efficiently.
C) Use an accelerated data science framework such as RAPIDS cuDF or Dask to distribute computations across GPUs.
D) Load the entire dataset into a Pandas DataFrame and analyze it using Pandas built-in methods.

4. You are tasked with processing a large dataset of 100 million records for a deep learning project using NVIDIA technologies. You need to determine the most efficient data processing library for this task to maximize performance and reduce processing time.
Which of the following libraries is best suited for this task?

A) Dask
B) PySpark
C) pandas
D) cuDF

5. A data scientist is working with a large dataset containing missing values and outliers. The dataset will be used for training a machine learning model. The scientist decides to preprocess the data using RAPIDS cuDF, an accelerated dataframe library.
Which of the following is the most efficient approach to handle missing values while maintaining data integrity?

A) Use df.fillna(df.mean()) to replace missing values with the column mean.
B) Use df.dropna() to remove all rows with missing values.
C) Replace missing values with zero using df.fillna(0).
D) Convert missing values to a separate categorical class using df.fillna("missing").

Solutions: