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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are tasked with profiling a deep learning model using NVIDIA's DLProf to identify performance bottlenecks and optimize resource utilization.
Which of the following statements correctly describes the capabilities of DLProf?

A) DLProf can generate detailed reports that highlight kernel-level execution times and GPU utilization trends.
B) DLProf only works with TensorFlow models and does not support PyTorch-based workloads.
C) DLProf requires significant modifications to the source code to collect profiling data.
D) DLProf is primarily designed for debugging model accuracy rather than performance analysis.

2. You are working with cuGraph to analyze a large social network dataset where users are represented as nodes, and their connections (friendships) are represented as edges. You need to determine the most efficient way to store and process the graph in cuGraph for high-performance analytics.
Which of the following graph representations is best suited for efficient processing in cuGraph?

A) Incidence Matrix representation
B) Compressed Sparse Row (CSR) format
C) Edge List stored in a Pandas DataFrame
D) Adjacency List representation using Python dictionaries

3. You are working on optimizing a deep learning model for inference on an NVIDIA GPU. You decide to use NVIDIA DLProf to profile the model and analyze its performance. After running DLProf, you review the generated reports and find that the GPU Utilization is significantly lower than expected.
Which of the following is the most likely reason for this issue, as indicated by the profiling data?

A) The GPU lacks sufficient VRAM, causing frequent memory swaps to system RAM.
B) The batch size is too large, leading to excessive memory allocation failures.
C) DLProf detected a high level of tensor core utilization, which generally indicates poor performance.
D) The model contains a large number of small, inefficient kernel launches that introduce overhead.

4. You are working with a data science project that requires GPU acceleration for machine learning tasks. Your team is facing challenges with software version conflicts between different dependencies when deploying the project on different systems.
Which of the following solutions should you consider to efficiently manage software dependencies and avoid conflicts? (Select two)

A) Set up a virtual machine for each different dependency configuration to isolate environments.
B) Use Docker to containerize the project, ensuring that the dependencies and environment are consistent across different systems.
C) Install GPU drivers on the host machine and rely on the local system environment for dependency management.
D) Manually install all dependencies directly on the host machine to avoid using dependency management tools.
E) Use Conda to create isolated environments for different versions of dependencies, ensuring version compatibility.

5. You are working on a dataset containing missing values, duplicate records, and inconsistent data types.
The dataset size is 15GB and you need to efficiently perform data cleansing operations such as:
- Handling missing values
- Dropping duplicates
- Converting data types
Which of the following approaches would be the most efficient way to perform these operations on an NVIDIA GPU?

A) Use Vaex to perform data cleansing, as it is optimized for large-scale datasets
B) Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame
C) Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods
D) Use pandas to load the dataset and perform the operations using standard pandas functions

Solutions: