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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q224-Q229):

NEW QUESTION # 224
A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A. Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
B. Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
C. Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
D. Train a generative adversarial network (GAN) using PyTorch and then use the generated samples in RAPIDS AI without any additional processing.

Answer: A

NEW QUESTION # 225
You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?

A. Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution
B. Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
C. Use Dask DataFrame to parallelize deep learning model training across multiple GPUs
D. Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs

Answer: B

NEW QUESTION # 226
A data scientist is using an NVIDIA RAPIDS-based data processing pipeline on a GPU cluster. They notice that the pipeline is not performing as expected and suspect a bottleneck.
Which of the following approaches would best help identify the source of the bottleneck?

A. Increase the batch size of data being processed without analyzing memory utilization.
B. Rely on nvidia-smi alone to check GPU utilization without considering other profiling tools.
C. Disable GPU parallelism and run computations on the CPU to compare performance.
D. Use nsys (NVIDIA Nsight Systems) to profile GPU kernel execution and memory transfer times.

Answer: D

NEW QUESTION # 227
A research team is analyzing large-scale social interactions and wants to identify strongly connected communities within a massive graph dataset using NVIDIA's cuGraph library.
Which method would be the most efficient for this task?

A. Apply cuGraph's Label Propagation Algorithm (LPA) to divide the graph into communities without predefining the number of clusters.
B. Use cuGraph's Louvain method to detect hierarchical communities based on modularity optimization.
C. Apply cuGraph's Dijkstra's algorithm to find the shortest paths between all nodes and group them into communities.
D. Run the cuGraph PageRank algorithm and classify nodes with high scores as community leaders.

Answer: B

NEW QUESTION # 228
A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of- memory (OOM) errors.
To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?

A. Increasing batch size without adjusting the optimizer settings
B. Storing all training data in GPU memory at once
C. Using single-precision (FP32) calculations for better accuracy
D. Using mixed precision training with automatic loss scaling

Answer: D

NEW QUESTION # 229
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