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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q256-Q261):

NEW QUESTION # 256
A financial services company is deploying an AI-driven risk assessment model using NVIDIA GPUs on a cloud platform. To optimize resource utilization and cost efficiency, they need to determine the best GPU deployment strategy.
Which of the following is the most effective approach?

A. Deploy separate full-GPU instances for each workload, even if they have variable compute demands.
B. Run all AI workloads on a single large GPU instance without any partitioning or workload separation.
C. Choose an on-demand cloud instance with an outdated GPU model to reduce costs, even if performance is compromised.
D. Deploy the model using NVIDIAAI Enterprise with MIG (Multi-Instance GPU) to allocate multiple workloads on a single GPU efficiently.

Answer: D

NEW QUESTION # 257
You are tasked with implementing data caching to reduce shuffle in an accelerated machine learning pipeline using NVIDIA technologies. You need to cache intermediate results after a shuffle operation in a distributed setting.
Which of the following is the best approach to minimize shuffle overhead and maximize performance?

A. Use RAPIDS cuDF to cache the shuffled data on disk and then re-load it from disk during subsequent stages.
B. Cache the data in GPU memory using RAPIDS cuDF for faster access, and leverage GPU-based partitioning to reduce shuffle size.
C. Use DALI to perform preprocessing and cache the output before the shuffle operation, thereby eliminating the need for shuffle.
D. Implement Spark's default disk caching to store shuffle results, allowing the GPU to access disk data directly.

Answer: A

NEW QUESTION # 258
You are working with a large dataset containing millions of rows, and you need to store it efficiently for fast read and write operations while maintaining compatibility with CuDF and pandas.
Which of the following file formats is the best choice for efficient columnar storage and GPU-accelerated processing?

A. JSON
B. CSV
C. TXT
D. Parquet

Answer: D

NEW QUESTION # 259
You are tasked with designing and implementing a benchmark to compare the performance of different deep learning frameworks, including TensorFlow, PyTorch, and JAX, using NVIDIA GPUs.
Which of the following is the most effective approach to ensure an accurate and fair comparison?

A. Run each framework with default settings to compare their out-of-the-box performance without any optimizations.
B. Compare training times only without considering throughput, power efficiency, or memory utilization.
C. Ensure identical hardware configurations, dataset preprocessing, and model architectures while leveraging NVIDIA's Nsight Systems and DLProf for analysis.
D. Use mixed precision (FP16) training only in TensorFlow to maximize performance while keeping other frameworks at FP32.

Answer: C

NEW QUESTION # 260
A financial institution is using cuGraph to analyze transaction data and detect potential fraudulent activity. The institution wants to identify users who have a high likelihood of being involved in suspicious activities based on the structure of their transactions.
Which of the following cuGraph algorithms would be the best choice for this task?

A. Spectral Clustering
B. Weakly Connected Components
C. Betweenness Centrality
D. Breadth-First Search (BFS)

Answer: C

NEW QUESTION # 261
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