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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

You are working with cloud-based GPUs to process a large dataset (terabytes in size) stored in Parquet format. One column represents a unique identifier (e.g., product ID), and it contains only positive integers ranging from 1 to 100,000.
Which of the following data types provides the best balance of memory efficiency and performance?

A. float64
B. float32
C. int8
D. uint16

Question 2

You have deployed a deep learning model for image classification in a production environment, but inference latency is high. You need to optimize the model to reduce response time while maintaining accuracy.
Which NVIDIA technology is best suited for this task?

A. NVIDIA DeepStream to process image classification models for low-latency inference in batch mode.
B. NVIDIA Clara Imaging to improve deep learning inference for image classification workloads.
C. NVIDIA TensorRT to optimize and accelerate deep learning inference by reducing model size and execution time.
D. NVIDIA RAPIDS cuML to optimize deep learning inference using GPU-accelerated ML algorithms.

Question 3

You are processing a large dataset using RAPIDS cuDF and Dask-cuDF on an NVIDIA GPU. Your profiling indicates that data transfer times between CPU and GPU are significantly slowing down your pipeline.
What is the most effective way to reduce this bottleneck?

A. Transfer data in multiple smaller chunks to the GPU instead of larger batches
B. Use cudf.read_parquet() instead of Pandas to load data directly into GPU memory
C. Increase the CPU RAM allocation to store more data before transferring to the GPU
D. Convert the dataset into a CSV format before transferring it to the GPU

Question 4

You are setting up a GPU-accelerated data science environment that includes NVIDIA RAPIDS, PyTorch, TensorFlow, and other libraries for machine learning and data processing.
Given that these frameworks have different dependencies and version requirements, what is the best approach to avoid software conflicts while ensuring reproducibility across multiple environments?

A. Use Conda to create isolated virtual environments for each project and install dependencies via conda-forge or NVIDIA channels.
B. Manually download and compile each library from source to guarantee compatibility across all versions.
C. Use a single Docker container with the latest versions of all dependencies installed system-wide.
D. Install all packages globally using pip on the system-wide Python installation to ensure consistency.

Question 5

A company is deploying an MLOps pipeline for training and serving deep learning models. The data scientists want to leverage GPU acceleration at multiple stages of the pipeline to enhance efficiency.
Which of the following steps would benefit the most from GPU acceleration?

A. Training and inference workloads using deep learning models with TensorFlow or PyTorch.
B. Model monitoring by logging metadata and performance metrics in a database.
C. Storing and retrieving models from a centralized object storage system.
D. Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.

Solutions: