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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. GPU-accelerated ETL design and implementation 2. Data caching and performance optimization 3. Distributed data processing frameworks (Dask)
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Perform time series analysis and visualization 2. Detect anomalies in time series datasets 3. Use cuGraph for graph analytics
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. CRISP-DM workflow execution 2. Docker and Conda environment management 3. Benchmarking GPU workflows
MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Performance benchmarking and optimization 3. Model deployment in production environments
Machine Learning	15%	- Model Development and Optimization 1. Feature engineering 2. Multi-GPU training comparison 3. Memory optimization techniques (mixed precision, batching) 4. Hyperparameter tuning
Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. Data normalization and standardization 3. cuDF and pandas data preprocessing

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q298-Q303):

NEW QUESTION # 298
A machine learning engineer is benchmarking a GPU-accelerated pipeline for data preprocessing and training. The pipeline consists of large-scale data transformations using RAPIDS cuDF and training a model with TensorFlow. The engineer wants to ensure that GPU utilization is maximized throughout the workflow.
Which approach is most effective in achieving this?

A. Pipeline execution should be asynchronous, overlapping data preprocessing with model training.
B. Precompute all features on the CPU before loading them into the GPU for training.
C. Use a single GPU to avoid overhead associated with multi-GPU synchronization.
D. Reduce the dataset size to fit entirely in GPU memory, even if it leads to data loss.

Answer: A

NEW QUESTION # 299
A research team is analyzing a large transportation network and wants to identify groups of locations that are closely connected based on travel routes.
Which NVIDIA cuGraph function would be the most effective for this task?

A. Use cuGraph's pagerank() to rank the most connected locations.
B. Use cuGraph's shortest_path() to compute all shortest paths and derive clusters manually.
C. Use cuGraph's louvain() function to detect communities within the graph.
D. Apply cuGraph's bfs() (Breadth-First Search) to find clusters of locations.

Answer: C

NEW QUESTION # 300
Which of the following techniques are best suited for efficiently processing and organizing large datasets using NVIDIA technologies? (Select two)

A. Leveraging Dask for distributed GPU-based parallel computing
B. Using TensorFlow with custom data pipelines for data loading
C. Using cuDF for GPU-accelerated DataFrame operations
D. Storing data directly on an HDD and processing with GPUs

Answer: A,C

NEW QUESTION # 301
You are deploying an NVIDIA GPU-accelerated machine learning model in a Docker container and want to ensure that your application can leverage the GPU efficiently.
What is the best way to manage CUDA dependencies and avoid compatibility issues inside your Docker container?

A. Manually install CUDA and cuDNN inside the container by downloading them from NVIDIA's website and setting environment variables.
B. Use a base Ubuntu image and install TensorFlow, PyTorch, and CUDA using pip install inside the container.
C. Disable GPU acceleration in Docker and force computations on the CPU to avoid CUDA compatibility issues.
D. Use NVIDIA's official Docker images from NVIDIA GPU Cloud (NGC), which come with pre-installed CUDA and AI frameworks.

Answer: D

NEW QUESTION # 302
In the CRISP-DM process, which NVIDIA technology is commonly used during the "Data Preparation" phase to handle large datasets efficiently?

A. NVIDIA Jetson
B. NVIDIA RAPIDS
C. NVIDIA CUDA Toolkit
D. NVIDIA Deep Learning AI

Answer: B

NEW QUESTION # 303
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