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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading
Topic 2: GPU and Cloud Computing	16%	- Performance optimization 1. Single and multi-GPU performance optimization 2. Mixed precision and bottleneck analysis 3. Memory profiling with DLProf - 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 - GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science
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: MLOps	19%	- 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 - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management
Topic 5: Machine Learning	15%	- Model training with GPU acceleration 1. Selection of appropriate algorithms for GPU execution 2. Multi-GPU training strategies 3. Training models using cuML and GPU-accelerated XGBoost - 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. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch
Topic 6: Data Analysis	14%	- Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q219-Q224):

NEW QUESTION # 219
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. Run the cuGraph PageRank algorithm and classify nodes with high scores as community leaders.
B. Apply cuGraph's Label Propagation Algorithm (LPA) to divide the graph into communities without predefining the number of clusters.
C. Apply cuGraph's Dijkstra's algorithm to find the shortest paths between all nodes and group them into communities.
D. Use cuGraph's Louvain method to detect hierarchical communities based on modularity optimization.

Answer: D

NEW QUESTION # 220
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. Increase the CPU RAM allocation to store more data before transferring to the GPU
B. Use cudf.read_parquet() instead of Pandas to load data directly into GPU memory
C. Transfer data in multiple smaller chunks to the GPU instead of larger batches
D. Convert the dataset into a CSV format before transferring it to the GPU

Answer: B

NEW QUESTION # 221
A data engineer is preparing a dataset for training a deep learning model. The dataset contains numerical features with missing values, outliers, and inconsistent units.
Which of the following strategies is the most appropriate for ensuring a standardized and clean dataset?

A. Standardize the dataset using the mean and standard deviation, but keep missing values and outliers unchanged to avoid data manipulation.
B. Remove all rows with missing values and outliers to ensure only clean data is used.
C. Replace missing values with the mean, apply z-score normalization, and clip extreme outliers based on a threshold (e.g., 3 standard deviations).
D. Use the median to fill missing values, convert all numerical values into categorical bins, and apply Min-Max scaling.

Answer: C

NEW QUESTION # 222
You are building a deep learning model using TensorFlow with cuDNN acceleration on an NVIDIA GPU. Your dataset contains continuous numerical features with vastly different ranges.
What is the best way to standardize the data efficiently to improve model convergence?

A. Use cupy.linalg.norm() to normalize each feature vector individually to unit length.
B. Apply batch normalization layers in the neural network to handle feature scaling dynamically during training.
C. Use cuml.StandardScaler() from RAPIDS to normalize the dataset before feeding it into the model.
D. Manually compute the feature mean and variance on the CPU and apply the transformation before training.

Answer: C

NEW QUESTION # 223
A machine learning team is handling large-scale datasets that need to be efficiently stored and accessed within an NVIDIA RAPIDS workflow.
Which of the following storage formats and techniques provides the best performance for GPU-based data science pipelines?

A. Store data in SQLite databases and query it into pandas before converting it to cuDF.
B. Store data in the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.
C. Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.
D. Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.

Answer: B

NEW QUESTION # 224
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