NCP-ADS Valid Test Materials, NCP-ADS PDF Cram Exam

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - Data loading and preprocessing 1. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines
Topic 2: Machine Learning	15%	- Model training with GPU acceleration 1. Multi-GPU training strategies 2. Training models using cuML and GPU-accelerated XGBoost 3. Selection of appropriate algorithms for GPU execution - 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
Topic 3: GPU and Cloud Computing	16%	- Performance optimization 1. Mixed precision and bottleneck analysis 2. Memory profiling with DLProf 3. Single and multi-GPU performance optimization - GPU resource management 1. Efficient GPU resource allocation and scheduling - GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud
Topic 4: Data Manipulation and Software Literacy	19%	- 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. cuDF vs pandas API mapping and usage 2. Groupby, apply, and aggregation operations 3. Data integration, joining, merging, and filtering - Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency
Topic 5: 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 - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization
Topic 6: MLOps	19%	- Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - 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 - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q285-Q290):

NEW QUESTION # 285
A data scientist is analyzing a large dataset of financial transactions containing millions of records.
To efficiently perform exploratory data analysis (EDA) using RAPIDS cuDF, which approach provides the most optimized performance while ensuring comprehensive insights?

A. Downsample the dataset and analyze a subset using Pandas for efficiency.
B. Use RAPIDS cuDF functions like .describe() and .value_counts() to perform statistical summaries directly on the GPU.
C. Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.
D. Perform all analysis on the CPU to avoid potential GPU memory limitations.

Answer: B

NEW QUESTION # 286
You are processing a large-scale transportation network graph using NVIDIA cuGraph. The graph is extremely large, consuming almost all available GPU memory. Performance is deteriorating, and some computations fail due to memory exhaustion.
What is the best approach to efficiently handle this large graph while keeping computations on the GPU?

A. Use cuGraph's multi-GPU support via Dask-cuGraph to distribute the graph across multiple GPUs.
B. Manually split the graph into chunks and process each chunk separately without any coordination.
C. Store the graph as a large Python dictionary and use cuGraph only for specific queries.
D. Convert the graph into a NetworkX graph and process it on the CPU to reduce GPU memory usage.

Answer: A

NEW QUESTION # 287
You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.
Which approach best utilizes NVIDIA technologies for efficient forecasting?

A. Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.
B. Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
C. Use cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.
D. Use PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.

Answer: C

NEW QUESTION # 288
A data scientist is working with datasets ranging from hundreds of megabytes to several terabytes and needs to select the most efficient NVIDIA-accelerated data processing library for optimal memory management and performance.
Which approach is best for selecting the appropriate library for different dataset sizes?

A. Use cuDF for small datasets and Dask-cuDF for larger datasets that do not fit in a single GPU's memory.
B. Use RAPIDS cuML for handling all data processing tasks, regardless of dataset size.
C. Always use Dask regardless of dataset size since Dask automatically scales from small to large datasets.
D. Use Pandas for all dataset sizes since Pandas has built-in multi-threading optimizations.

Answer: A

NEW QUESTION # 289
After profiling a deep learning model using NVIDIA DLProf, you notice that a specific GEMM (General Matrix Multiplication) operation takes significantly longer than expected. The profiler output reveals that tensor cores are underutilized despite having an Ampere-based GPU with Tensor Cores enabled.
Which of the following actions is the MOST appropriate to improve performance?

A. Switch from stochastic gradient descent (SGD) to Adam optimizer, as Adam improves convergence and computational efficiency.
B. Convert the model's data type to float16 or bfloat16 and re-run the training with automatic mixed precision (AMP).
C. Disable CUDA graphs and enforce PyTorch's eager execution mode to improve kernel execution order.
D. Increase the batch size to maximize GPU memory usage and reduce kernel launch overhead.

Answer: B

NEW QUESTION # 290
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