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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science - Performance optimization 1. Single and multi-GPU performance optimization 2. Mixed precision and bottleneck analysis 3. Memory profiling with DLProf - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - GPU resource management 1. Efficient GPU resource allocation and scheduling
Data Manipulation and Software Literacy	19%	- 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) - Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs
Data Analysis	14%	- Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis
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 - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools
Machine Learning	15%	- Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - Model training with GPU acceleration 1. Multi-GPU training strategies 2. Selection of appropriate algorithms for GPU execution 3. Training models using cuML and GPU-accelerated XGBoost - Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Feature engineering for ML models 3. Batching and memory-efficient training methods
Data Preparation	17%	- Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - 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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q19-Q24):

NEW QUESTION # 19
You are tasked with profiling a PyTorch-based deep learning model to identify performance bottlenecks using NVIDIA DLProf. Your goal is to analyze kernel execution times and identify operations causing excessive memory consumption.
Which of the following steps is the MOST appropriate sequence for profiling using DLProf?

A. Execute the training script under DLProf TensorBoard mode to visualize performance insights, then re-run the model with automatic mixed precision (AMP) to reduce memory usage.
B. Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.
C. Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
D. Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.

Answer: C

NEW QUESTION # 20
You are working with a social network dataset containing millions of user interactions and need to identify influential users based on their connectivity and interactions.
Which approach using NVIDIA's cuGraph library is the most appropriate for this task?

A. Use cuGraph's DBSCAN clustering to detect communities in the social network.
B. Run a breadth-first search (BFS) on the entire graph to find the most influential users.
C. Apply cuGraph's K-Means clustering to group users with similar connectivity patterns.
D. Use cuGraph's PageRank algorithm to rank users based on their importance in the network.

Answer: D

NEW QUESTION # 21
You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A. Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.
B. Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
C. Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
D. Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.

Answer: B

NEW QUESTION # 22
You are comparing the performance of GPU-accelerated deep learning models on two cloud platforms: AWS EC2 and Google Cloud Platform (GCP). You want to design a benchmark that evaluates GPU resource utilization, processing time, and cost-efficiency for training models with large datasets.
Which actions should you take to implement an effective benchmark? (Select two)

A. Only consider the cost of GPU resources for each cloud provider without factoring in data storage and transfer costs.
B. Measure both GPU utilization and network latency between the cloud platform and your data storage location.
C. Run each model on the same GPU type and instance size across both cloud platforms.
D. Limit the benchmarking tests to a single model architecture for each cloud platform.

Answer: B,C

NEW QUESTION # 23
You have trained a machine learning model using cuML as part of the Modeling phase in the CRISP- DM framework. Now, you need to assess how well the model performs before moving forward with deployment.
Which of the following steps aligns best with the Evaluation phase of CRISP-DM using NVIDIA technologies?

A. Define the problem statement and collect relevant datasets before training the model.
B. Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.
C. Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().
D. Deploy the model to an edge device using TensorRT for real-time inference.

Answer: C

NEW QUESTION # 24
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