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NVIDIA NCP-ADS Questions & Answers - in .pdf

NCP-ADS pdf
  • Total Q&A: 303
  • Update: Aug 23, 2026
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  • Vendor: NVIDIA
  • Exam Code: NCP-ADS
  • Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
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NVIDIA NCP-ADS Q&A - Testing Engine

NCP-ADS Study Guide
  • Total Q&A: 303
  • Update: Aug 23, 2026
  • Price: $59.99
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  • Vendor: NVIDIA
  • Exam Code: NCP-ADS
  • Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
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NVIDIA NCP-ADS Exam Overview:

Certification Vendor:NVIDIA
Exam Name:NVIDIA-Certified Professional Accelerated Data Science
Exam Number:NCP-ADS
Certificate Validity Period:2 years
Real Exam Qty:60-70
Exam Format:Multiple-choice
Available Languages:English
Passing Score:~70%
Exam Duration:120 minutes
Related Certifications:NVIDIA-Certified Professional: Accelerated Data Science
Exam Price:$200 USD
Sample Questions:NVIDIA NCP-ADS Sample Questions
Exam Way:Online, remotely proctored
Pre Condition:Two to three years of hands-on experience in accelerated data science. Strong foundation in machine learning and GPU-accelerated computing. Experience in GPU-based optimization strategies and accelerated data manipulation techniques. Deep understanding of end-to-end data science workflows, from data preparation and cleansing to model development and deployment.
Official Syllabus URL:https://www.nvidia.com/en-us/learn/certification/accelerated-data-science-professional

NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
MLOps19%- Containerization and environment management
  • 1. Conda environment management
  • 2. Docker for reproducible GPU-accelerated workflows
- Model monitoring and management
  • 1. Managing model artifacts and configurations for reproducibility
  • 2. Monitoring production models for drift and performance degradation
- 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
Data Analysis14%- Exploratory data analysis
  • 1. Descriptive statistics and summary analysis
  • 2. Performing EDA on GPU-accelerated datasets
- 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
- Graph analytics
  • 1. Creating and analyzing graph data using cuGraph
  • 2. Node importance evaluation and network relationship visualization
Machine Learning15%- Feature engineering and hyperparameter tuning
  • 1. Feature engineering for ML models
  • 2. Hyperparameter tuning techniques
  • 3. Batching and memory-efficient training methods
- Model training with GPU acceleration
  • 1. Multi-GPU training strategies
  • 2. Selection of appropriate algorithms for GPU execution
  • 3. Training models using cuML and GPU-accelerated XGBoost
- Deep learning frameworks integration
  • 1. Using RAPIDS with TensorFlow and PyTorch
  • 2. Overfitting vs underfitting concepts
Data Preparation17%- GPU-accelerated ETL workflows
  • 1. Efficient processing and storage with Parquet
  • 2. RAPIDS-based ETL pipelines
- Data cleaning and quality handling
  • 1. Data governance and compliance
  • 2. Handling missing values and data quality issues
- Feature engineering
  • 1. Feature engineering for numerical and categorical variables
  • 2. Dimensionality reduction and data sampling
- Data loading and preprocessing
  • 1. NVIDIA DALI for high-performance data loading
  • 2. Handling class imbalance and generating synthetic data
GPU and Cloud Computing16%- GPU architecture and fundamentals
  • 1. CPU vs GPU workloads and memory transfer optimization
  • 2. GPU architecture fundamentals for data science
- GPU resource management
  • 1. Efficient GPU resource allocation and scheduling
- Cloud GPU environments
  • 1. Cloud-based GPU instance configuration
  • 2. Containerized workflow deployment on cloud
- Performance optimization
  • 1. Mixed precision and bottleneck analysis
  • 2. Memory profiling with DLProf
  • 3. Single and multi-GPU performance optimization
Data Manipulation and Software Literacy19%- 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

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

A) Business Understanding
B) Model Building
C) Data Understanding
D) Data Preparation


2. You are working on an MLOps pipeline with a large dataset using NVIDIA technologies. You need to assess the memory size of the dataset before training the model to avoid memory overflow errors.
Which of the following is the best method for verifying the memory size of the dataset on the GPU using NVIDIA tools?

A) Use PyTorch's torch.cuda.memory_allocated() function to check the memory allocated on the GPU after the dataset is loaded.
B) Use cuDF to load the dataset into GPU memory and use the nvidia-smi tool to monitor GPU memory usage during dataset loading.
C) Use NVIDIA DALI to process the dataset and directly query the memory size through DALI's DataLoader function.
D) Load the dataset into a CPU-based pandas dataframe and use the pandas.memory_usage() function to estimate the dataset's size before transferring it to the GPU.


3. You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A) Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
B) Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.
C) Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.
D) Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.


4. You are training a machine learning model using RAPIDS cuML and need to ensure that all numeric features are standardized for better model performance.
Which of the following is the best approach for scaling data using RAPIDS?

A) df_scaled = scaler.fit_transform(df)
B) df_scaled = df / df.max()
C) scaler = cuml.preprocessing.StandardScaler()
D) df_scaled = df.apply(lambda x: x / np.linalg.norm(x))
E) df_scaled = (df - df.min()) / (df.max() - df.min())


5. Which of the following best describes the functionality of DLProf in deep learning model profiling?

A) DLProf tracks GPU utilization, memory bandwidth, and kernel execution time to identify performance bottlenecks.
B) DLProf is used for visualizing model predictions, similar to TensorBoard.
C) DLProf uses statistical methods to predict future model performance based on historical data.
D) DLProf can only profile the CPU side of a deep learning model's performance.


Solutions:

Question # 1
Answer: A
Question # 2
Answer: B
Question # 3
Answer: D
Question # 4
Answer: C
Question # 5
Answer: A

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NCP-ADS Related Exams
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NCP-AII - NVIDIA AI Infrastructure
NCP-AIO - NVIDIA AI Operations
NCP-OUSD - NVIDIA-Certified Professional: OpenUSD Development (NCP-OUSD)
NCP-AIN - NVIDIA-Certified Professional AI Networking
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