NCP-ADS Exam with Accurate NVIDIA-Certified-Professional Accelerated Data Science PDF Questions

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
MLOps	19%	- 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. 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
Data Preparation	17%	- 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 - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling
Data Analysis	14%	- 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 - 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
Data Manipulation and Software Literacy	19%	- GPU-accelerated data manipulation using cuDF 1. Data integration, joining, merging, and filtering 2. cuDF vs pandas API mapping and usage 3. Groupby, apply, and aggregation operations - Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs - Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
Machine Learning	15%	- Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - 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
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 - Performance optimization 1. Single and multi-GPU performance optimization 2. Mixed precision and bottleneck analysis 3. Memory profiling with DLProf - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q289-Q294):

NEW QUESTION # 289
A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A. Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
B. Manually generate random values using Python's built-in random module and load them into a cuDF DataFrame.
C. Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
D. Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.

Answer: C

NEW QUESTION # 290
You are working with a cuDF DataFrame and need to convert a column named sales from float64 to int32 to save memory.
Which of the following is the correct and most efficient way to perform this conversion in cuDF?

A. df['sales'].apply(lambda x: int(x))
B. df['sales'].convert_dtypes('int32')
C. df['sales'] = df['sales'].astype('int32')
D. df['sales'] = df['sales'].to_numeric('int32')

Answer: C

NEW QUESTION # 291
Which of the following Nvidia technologies is commonly used for deploying machine learning models in production environments, enabling scalable deployment and monitoring?

A. Nvidia Triton Inference Server
B. Nvidia DeepStream
C. Nvidia Jetson Nano
D. Nvidia CUDA

Answer: A

NEW QUESTION # 292
You are tasked with profiling a deep learning model using NVIDIA's DLProf to identify performance bottlenecks and optimize resource utilization.
Which of the following statements correctly describes the capabilities of DLProf?

A. DLProf only works with TensorFlow models and does not support PyTorch-based workloads.
B. DLProf is primarily designed for debugging model accuracy rather than performance analysis.
C. DLProf can generate detailed reports that highlight kernel-level execution times and GPU utilization trends.
D. DLProf requires significant modifications to the source code to collect profiling data.

Answer: C

NEW QUESTION # 293
You are preprocessing a dataset using NVIDIA RAPIDS cuDF and need to handle missing values in the column temperature by replacing them with the column's median value.
Which of the following approaches correctly achieves this in an optimized manner?

A. 1. df['temperature'] = df['temperature'].map(2. lambda x: df['temperature'].median() if x is None else x
3.)
B. df['temperature'].fillna(df['temperature'].mean(), inplace=True)
C. df['temperature'].dropna(inplace=True)
D. df['temperature'].fillna(df['temperature'].median(), inplace=True)

Answer: D

NEW QUESTION # 294
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