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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: GPU and Cloud Computing	16%	- Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science - Performance optimization 1. Mixed precision and bottleneck analysis 2. Single and multi-GPU performance optimization 3. Memory profiling with DLProf - GPU resource management 1. Efficient GPU resource allocation and scheduling
Topic 2: Data Manipulation and Software Literacy	19%	- Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency - Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs - GPU-accelerated data manipulation using cuDF 1. Data integration, joining, merging, and filtering 2. cuDF vs pandas API mapping and usage 3. Groupby, apply, and aggregation operations
Topic 3: Data Analysis	14%	- Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph
Topic 4: Machine Learning	15%	- Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Feature engineering for ML models 3. Batching and memory-efficient training methods - Model training with GPU acceleration 1. Selection of appropriate algorithms for GPU execution 2. Training models using cuML and GPU-accelerated XGBoost 3. Multi-GPU training strategies
Topic 5: MLOps	19%	- Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - Model deployment and serving 1. Production deployment strategies 2. Model saving, loading, and prediction generation - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation
Topic 6: Data Preparation	17%	- GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q204-Q209):

NEW QUESTION # 204
You are working on an AI-driven customer behavior prediction project.
According to the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, what is the most critical task to complete during the Data Understanding phase?

A. Deploying the model into a production environment for real-time inference.
B. Identifying and preparing feature engineering strategies to improve model accuracy.
C. Acquiring and exploring the dataset to assess quality, completeness, and potential biases.
D. Selecting the most appropriate machine learning algorithm for the prediction task.

Answer: C

NEW QUESTION # 205
You are a data scientist analyzing a social media network with NVIDIA cuGraph to identify the most influential users using the PageRank algorithm.
Which option best describes how cuGraph PageRank operates on a directed graph?

A. PageRank assigns equal importance to all nodes in the graph initially and updates values only based on outgoing edges, ignoring incoming edges.
B. PageRank in cuGraph uses an iterative power method to update node importance values based on incoming edges, incorporating a damping factor to handle random jumps.
C. PageRank in cuGraph is a label propagation algorithm that clusters nodes into communities rather than ranking their importance.
D. PageRank in cuGraph operates only on undirected graphs and cannot be applied to networks where edges have a direction.

Answer: B

NEW QUESTION # 206
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. Run the container using the default Docker runtime without any additional configurations
B. Install GPU drivers inside the container to ensure access to the host's hardware
C. Use a standard Python-based container image instead of an NVIDIA GPU-optimized image
D. Use the NVIDIA Container Toolkit (nvidia-docker2) to allow GPU access within Docker containers

Answer: D

NEW QUESTION # 207
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. Use Dask DataFrame to parallelize deep learning model training across multiple GPUs
B. Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
C. Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs
D. Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution

Answer: B

NEW QUESTION # 208
You are tasked with optimizing the performance of an MLOps pipeline that uses GPU-accelerated workflows. After running initial benchmarks, you notice that the training time is higher than expected, despite the use of multiple GPUs.
What are the best strategies to optimize the GPU-accelerated workflow in this case? (Select two)

A. Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.
B. Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.
C. Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.
D. Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
E. Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.

Answer: B,D

NEW QUESTION # 209
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