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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following is the most appropriate way to perform large-scale data processing in a GPU- accelerated environment using NVIDIA RAPIDS?

A) Use pandas for all data manipulations and rely on multi-threading for parallel execution.
B) Use NumPy exclusively for processing large datasets on GPUs.
C) Use Dask on top of RAPIDS for distributed computing across multiple GPUs.
D) Use TensorFlow for all data manipulations in a GPU environment.

2. You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?

A) Store all data as CSV files and perform ETL operations using traditional row-based processing.
B) Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
C) Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage.
D) Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.

3. You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

A) df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)
B) df = (df - df.min()) / (df.max() - df.min())
C) 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)
D) df = (df - df.mean()) / df.std()

4. Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

A) Data Understanding
B) Model Building
C) Data Preparation
D) Business Understanding

5. You are working with structured tabular data in a cloud-based GPU environment.
Your dataset contains the following columns:
Column Name Example Values Data Type Needed
user_id 15432, 98765, 43210 Integer
purchase_amt 12.99, 35.50, 100.75 Floating Point
category 'Books', 'Electronics' Categorical
Which of the following is the most optimal approach to assign data types to these columns to ensure efficient memory usage and computational performance?

A) 1. df['user_id'] = df['user_id'].astype('int16')
2. df['purchase_amt'] = df['purchase_amt'].astype('float16')
3. df['category'] = df['category'].astype('string')
B) 1. df['user_id'] = df['user_id'].astype('float32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
C) 1. df['user_id'] = df['user_id'].astype('int32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float32')
3. df['category'] = df['category'].astype('category')
D) 1. df['user_id'] = df['user_id'].astype('int64')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')

Solutions: