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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a dataset consisting of 100 million records stored in a distributed system. The dataset includes numerical and categorical variables, requiring both exploratory data analysis (EDA) and machine learning model training. The processing time using traditional CPU-based methods is too slow.
Which of the following techniques would be the most effective acceleration method to handle this workload efficiently?

A) Reduce the dataset to a smaller sample size before processin
B) Scale up to a high-core-count CPU machine
C) Store the dataset in a relational database and query it using SQL
D) Use RAPIDS cuDF for GPU-accelerated data processing

2. You are preprocessing a dataset using NVIDIA RAPIDS cuDF and need to handle missing values in the column temperature by replacing them with the column's median value.
Which of the following approaches correctly achieves this in an optimized manner?

A) df['temperature'].dropna(inplace=True)
B) df['temperature'].fillna(df['temperature'].median(), inplace=True)
C) 1. df['temperature'] = df['temperature'].map(2. lambda x: df['temperature'].median() if x is None else x
3.)
D) df['temperature'].fillna(df['temperature'].mean(), inplace=True)

3. A machine learning engineer is training a convolutional neural network (CNN) on an NVIDIA GPU and needs to maximize throughput while avoiding OOM errors.
Which of the following techniques is the most effective way to balance memory efficiency and training speed?

A) Using dynamic batch sizing based on available GPU memory
B) Using a batch size of 1 to minimize memory usage
C) Loading all dataset samples into GPU memory at the start of training
D) Allocating a fixed batch size without monitoring memory usage

4. You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?

A) Perform all EDA using NumPy and SciPy for optimized array computations.
B) Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU.
C) Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.
D) Use Pandas directly for data manipulation and visualization.

5. You are monitoring a GPU-accelerated ETL pipeline using RAPIDS cuDF and Dask-cuDF. You suspect that a bottleneck is causing the pipeline to slow down.
Which of the following methods is the most effective way to diagnose performance bottlenecks in your data processing pipeline?

A) Use NVIDIA Nsight Systems to profile GPU utilization and identify potential kernel execution inefficiencies
B) Increase the batch size of data loading without checking GPU memory usage
C) Use print() statements in the code to manually track execution times of different operation
D) Monitor CPU usage in the system to detect high CPU load that might indicate a bottleneck

Solutions: