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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A machine learning engineer is working with a 1 TB dataset stored in Apache Parquet format and wants to analyze the data for patterns before building a model. The engineer is considering various acceleration methods.
Which of the following approaches would be the best choice for efficient analysis?

A) Convert the Parquet file to a Pandas DataFrame and perform analysis using Pandas functions.
B) Read the Parquet file line by line using Python's built-in file handling functions to save memory.
C) Use a GPU-accelerated library such as RAPIDS cuDF to load and process the Parquet file efficiently.
D) Load the dataset into a relational database and query it using simple SQL statements.

2. You need to deploy a containerized machine learning model that utilizes NVIDIA GPUs on a cloud- based Kubernetes cluster.
Which of the following steps is essential for ensuring proper GPU utilization inside a Docker container?

A) Use a standard Python-based container image instead of an NVIDIA GPU-optimized image
B) Run the container using the default Docker runtime without any additional configurations
C) Use the NVIDIA Container Toolkit (nvidia-docker2) to allow GPU access within Docker containers
D) Install GPU drivers inside the container to ensure access to the host's hardware

3. You are working on a medium-sized dataset (~500,000 rows, 20 columns) and need to perform fast exploratory data analysis (EDA) with filtering, aggregations, and transformations.
Which of the following Python libraries would be the most efficient choice for this task?

A) PySpark
B) Vaex
C) Dask
D) Pandas

4. A data scientist is working on a social network analysis project where they need to find the most influential users in a large-scale graph dataset. The dataset consists of millions of users connected through directed edges.
Which of the following approaches would be the best choice for this task using NVIDIA GPU-accelerated tools?

A) Use cuDF's groupby().sum() function to count the number of connections per user.
B) Use cuGraph's pagerank() function to identify influential nodes based on link structure.
C) Use cuGraph's bfs() (Breadth-First Search) to find the most influential nodes.
D) Convert the graph into a pandas DataFrame and apply NetworkX's PageRank implementation.

5. You are working on a large dataset for a machine learning model that will be trained using RAPIDS cuML. The dataset includes categorical, integer, and floating-point features.
Which of the following approaches is the best practice for determining the optimal data type choice for each feature using NVIDIA's RAPIDS cuDF library?

A) Convert all numerical data to float64 for maximum precision in calculations.
B) Use float32 instead of float64 for floating-point numbers when possible, and leverage int8, int16, or int32 for categorical and integer data based on their range.
C) Convert categorical variables into int8 to optimize GPU memory usage.
D) Use float16 for all floating-point data to reduce memory usage and increase GPU processing speed.

Solutions: