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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting
Topic 2: Machine Learning	15%	- Feature engineering and hyperparameter tuning 1. Feature engineering for ML models 2. Batching and memory-efficient training methods 3. Hyperparameter tuning techniques - Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - 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
Topic 3: 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. cuDF vs pandas API mapping and usage 2. Data integration, joining, merging, and filtering 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 4: 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 - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines
Topic 5: GPU and Cloud Computing	16%	- GPU resource management 1. Efficient GPU resource allocation and scheduling - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - Performance optimization 1. Single and multi-GPU performance optimization 2. Memory profiling with DLProf 3. Mixed precision and bottleneck analysis - GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science
Topic 6: 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 - 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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q109-Q114):

NEW QUESTION # 109
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. Storing data directly on an HDD and processing with GPUs
C. Using cuDF for GPU-accelerated DataFrame operations
D. Using TensorFlow with custom data pipelines for data loading

Answer: A,C

NEW QUESTION # 110
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. Use cudf.read_parquet() instead of Pandas to load data directly into GPU memory
B. Convert the dataset into a CSV format before transferring it to the GPU
C. Transfer data in multiple smaller chunks to the GPU instead of larger batches
D. Increase the CPU RAM allocation to store more data before transferring to the GPU

Answer: A

NEW QUESTION # 111
You are working with a large dataset containing numeric and categorical features, which will be processed using NVIDIA RAPIDS cuDF for accelerated analytics.
To optimize performance while minimizing memory usage, which data type is the most appropriate for storing a categorical variable with a small number of unique values?

A. float32 - Reduces memory consumption compared to float64 while maintaining precision.
B. bool - Minimizes memory usage and supports efficient operations for categorical data.
C. int64 - Provides high precision and avoids potential overflow.
D. category - Optimizes storage and computation for categorical data in cuDF.

Answer: D

NEW QUESTION # 112
Which of the following statements best describes the role of GPUs in accelerating data science workloads?

A. GPUs are optimized for sequential data processing tasks, making them more efficient than CPUs for database operations.
B. GPUs are designed primarily for rendering graphics and have limited utility in machine learning and deep learning applications.
C. GPUs are only effective for acceleration when used in conjunction with Tensor Processing Units (TPUs), as they cannot train deep learning models independently.
D. GPUs use thousands of smaller cores that can execute many parallel computations simultaneously, making them ideal for large-scale matrix operations.

Answer: D

NEW QUESTION # 113
You are preprocessing a dataset using NVIDIA RAPIDS cuDF and need to handle missing values in the column temperature by replacing them with the column's median value.
Which of the following approaches correctly achieves this in an optimized manner?

A. df['temperature'].dropna(inplace=True)
B. df['temperature'].fillna(df['temperature'].mean(), inplace=True)
C. 1. df['temperature'] = df['temperature'].map(2. lambda x: df['temperature'].median() if x is None else x
3.)
D. df['temperature'].fillna(df['temperature'].median(), inplace=True)

Answer: D

NEW QUESTION # 114
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