Reliable NCP-ADS Learning Materials - NCP-ADS Valid Exam Materials

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Analysis	14%	- Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting
Machine Learning	15%	- Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Hyperparameter tuning techniques 3. Feature engineering for ML models - 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 - Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch
MLOps	19%	- 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. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware
GPU and Cloud Computing	16%	- Performance optimization 1. Mixed precision and bottleneck analysis 2. Single and multi-GPU performance optimization 3. Memory profiling with DLProf - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - GPU architecture and fundamentals 1. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization - GPU resource management 1. Efficient GPU resource allocation and scheduling
Data Preparation	17%	- GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - 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
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. 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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q155-Q160):

NEW QUESTION # 155
A company is deploying an MLOps pipeline for training and serving deep learning models. The data scientists want to leverage GPU acceleration at multiple stages of the pipeline to enhance efficiency.
Which of the following steps would benefit the most from GPU acceleration?

A. Storing and retrieving models from a centralized object storage system.
B. Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.
C. Model monitoring by logging metadata and performance metrics in a database.
D. Training and inference workloads using deep learning models with TensorFlow or PyTorch.

Answer: D

NEW QUESTION # 156
Which of the following tools in the NVIDIA AI stack is specifically designed to accelerate the deployment of machine learning models for production by optimizing inference performance?

A. cuBLAS
B. DLA (Deep Learning Accelerator)
C. cuML
D. Triton Inference Server

Answer: D

NEW QUESTION # 157
A financial services company is using NVIDIA RAPIDS cuML to train a credit risk assessment model.
The dataset contains hundreds of numerical and categorical features, including loan amount, credit score, income, employment history, and previous loan defaults.
To optimize feature selection using NVIDIA technologies, which approach should they take?

A. Convert all numerical features into categorical variables to simplify the data and reduce the need for feature selection.
B. Keep all features in the dataset to ensure the model captures as much information as possible, even if some features are redundant.
C. Avoid feature selection, as modern deep learning models are capable of automatically handling redundant and irrelevant features.
D. Use cuML's feature selection algorithms, such as Recursive Feature Elimination (RFE), to identify the most important predictors.

Answer: D

NEW QUESTION # 158
A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?

A. Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies
B. Manually installing each package and its dependencies using pip
C. Using a single system-wide installation of CUDA and forcing all projects to use the same version
D. Using only virtual environments (venv) without managing GPU dependencies separately

Answer: A

NEW QUESTION # 159
You are processing a multi-terabyte dataset in CuDF and want to optimize query performance and storage efficiency.
Which approach should you follow to ensure that the dataset remains efficiently partitioned and easily accessible?

A. Store the entire dataset in a single large file to minimize the number of files in the directory.
B. Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.
C. Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.
D. Split the dataset into multiple files based on a logical partitioning key, such as a date column.

Answer: D

NEW QUESTION # 160
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