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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis
Topic 2: GPU and Cloud Computing	16%	- 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. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - Performance optimization 1. Memory profiling with DLProf 2. Single and multi-GPU performance optimization 3. Mixed precision and bottleneck analysis
Topic 3: MLOps	19%	- Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - 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
Topic 4: Machine Learning	15%	- 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 - Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Feature engineering for ML models 3. Hyperparameter tuning techniques - Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts
Topic 5: Data Manipulation and Software Literacy	19%	- 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 - 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
Topic 6: Data Preparation	17%	- 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 - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q83-Q88):

NEW QUESTION # 83
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. Model monitoring by logging metadata and performance metrics in a database.
B. Storing and retrieving models from a centralized object storage system.
C. Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.
D. Training and inference workloads using deep learning models with TensorFlow or PyTorch.

Answer: D

NEW QUESTION # 84
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. Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.
B. Split the dataset into multiple files based on a logical partitioning key, such as a date column.
C. Store the entire dataset in a single large file to minimize the number of files in the directory.
D. Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.

Answer: B

NEW QUESTION # 85
You are working on an AI-driven customer behavior prediction project.
According to the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, what is the most critical task to complete during the Data Understanding phase?

A. Deploying the model into a production environment for real-time inference.
B. Identifying and preparing feature engineering strategies to improve model accuracy.
C. Selecting the most appropriate machine learning algorithm for the prediction task.
D. Acquiring and exploring the dataset to assess quality, completeness, and potential biases.

Answer: D

NEW QUESTION # 86
Which feature of NVIDIA MLFlow integration with Triton Inference Server allows for the seamless deployment and monitoring of models in production?

A. MLFlow handles only the training and not the deployment of models.
B. MLFlow only supports deployment on CPUs.
C. MLFlow prevents models from being updated once deployed to production.
D. MLFlow allows for version control and automated model rollout.

Answer: D

NEW QUESTION # 87
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. Avoid feature selection, as modern deep learning models are capable of automatically handling redundant and irrelevant features.
B. Use cuML's feature selection algorithms, such as Recursive Feature Elimination (RFE), to identify the most important predictors.
C. Convert all numerical features into categorical variables to simplify the data and reduce the need for feature selection.
D. Keep all features in the dataset to ensure the model captures as much information as possible, even if some features are redundant.

Answer: B

NEW QUESTION # 88
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