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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Manipulation and Software Literacy	19%	- Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) - GPU-accelerated data manipulation using cuDF 1. Data integration, joining, merging, and filtering 2. Groupby, apply, and aggregation operations 3. cuDF vs pandas API mapping and usage - Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing
Data Preparation	17%	- Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - 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. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data
MLOps	19%	- Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools
GPU and Cloud Computing	16%	- Performance optimization 1. Memory profiling with DLProf 2. Mixed precision and bottleneck analysis 3. Single and multi-GPU performance optimization - 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 - GPU resource management 1. Efficient GPU resource allocation and scheduling
Data Analysis	14%	- Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph
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 - Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - 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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q24-Q29):

NEW QUESTION # 24
You are processing a dataset with billions of records and want to encode a categorical column efficiently using NVIDIA RAPIDS.
Which of the following methods correctly encodes categorical data using cuDF?

A. df['category_column'] = df['category_column'].one_hot_encode()
B. df['category_column'] = df['category_column'].astype('category')
C. df['category_column'] = df['category_column'].apply(lambda x: hash(x) % 1000)
D. df['category_column'] = LabelEncoder().fit_transform(df['category_column'])

Answer: B

NEW QUESTION # 25
Which of the following Nvidia technologies is primarily used for performing benchmarking and optimizing GPU-accelerated deep learning workflows, especially focusing on model training performance?

A. Nvidia Nsight Systems
B. Nvidia CUDA
C. Nvidia Triton Inference Server
D. Nvidia DeepStream SDK

Answer: A

NEW QUESTION # 26
A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A. Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.
B. Reducing dataset size by randomly removing data points without considering class balance.
C. Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.
D. Using a single-threaded feature extraction approach to avoid overhead from parallelization.

Answer: A

NEW QUESTION # 27
You are working on a data science project that requires augmenting a dataset using synthetic data.
You are utilizing cuDF and NVIDIA RAPIDS to speed up the data generation process.
Which of the following methods is the most effective way to generate synthetic data using cuDF in a RAPIDS workflow?

A. Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.
B. Use cuDF to manipulate data distributions and generate new data points based on existing features.
C. Use cudf.DataFrame.applymap() to create new synthetic features through complex mathematical functions.

Answer: B

NEW QUESTION # 28
A data scientist is working with a large dataset that contains string-based numeric values that need to be converted to floating-point numbers for further analysis. The dataset is stored as a cuDF DataFrame, and the scientist needs to ensure the conversion is performed optimally on a GPU.
Which of the following is the best method for converting string-based numeric values to floating-point numbers using NVIDIA-accelerated processing?

A. Convert the cuDF DataFrame to a Pandas DataFrame first, then apply astype(float) and convert it back to cuDF.
B. Use pandas.to_numeric() since pandas automatically handles type conversion.
C. Use cudf.DataFrame.astype(float) to convert string values to floating-point numbers efficiently on a GPU.
D. Use NumPy's astype(float) method after converting the cuDF DataFrame into a NumPy array.

Answer: C

NEW QUESTION # 29
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