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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q83-Q88):

NEW QUESTION # 83
Which of the following is the most efficient way to implement data parallelism using Dask for multi- GPU scaling on an Nvidia platform?

A. Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.
B. Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.
C. Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.
D. Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.

Answer: D

NEW QUESTION # 84
A data scientist is working on a social network analysis project where they need to find the most influential users in a large-scale graph dataset. The dataset consists of millions of users connected through directed edges.
Which of the following approaches would be the best choice for this task using NVIDIA GPU-accelerated tools?

A. Convert the graph into a pandas DataFrame and apply NetworkX's PageRank implementation.
B. Use cuGraph's pagerank() function to identify influential nodes based on link structure.
C. Use cuGraph's bfs() (Breadth-First Search) to find the most influential nodes.
D. Use cuDF's groupby().sum() function to count the number of connections per user.

Answer: B

NEW QUESTION # 85
You are tasked with optimizing a data science workflow to scale across multiple GPUs using Dask.
Which of the following approaches would be most effective for implementing data parallelism in this scenario? (Select two)

A. Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing
B. Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations
C. Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
D. Use Dask's default scheduler to distribute work across GPUs without any specific configuration
E. Run the computation on a single GPU and let Dask handle CPU parallelism automatically

Answer: A,B

NEW QUESTION # 86
A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?

A. Precompute the time-series visualization with Dask and display it in a static HTML page.
B. Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.
C. Rely on Matplotlib to generate static plots and update them every minute with a loop.
D. Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.

Answer: B

NEW QUESTION # 87
You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?

A. Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF
B. Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution
C. Use cuDF's .fillna() method to replace missing values in GPU memory
D. Drop all missing values using .dropna() instead of filling them, as GPU memory is limited

Answer: C

NEW QUESTION # 88
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