NVIDIA NCP-ADS dumps - in .pdf

NCP-ADS pdf
  • Exam Code: NCP-ADS
  • Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
  • Updated: Sep 09, 2026
  • Q & A: 303 Questions and Answers
  • PDF Price: $59.99
  • Free Demo

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  • Exam Code: NCP-ADS
  • Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
  • Updated: Sep 09, 2026
  • Q & A: 303 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $119.98  $79.99
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NVIDIA NCP-ADS dumps - Testing Engine

NCP-ADS Testing Engine
  • Exam Code: NCP-ADS
  • Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
  • Updated: Sep 09, 2026
  • Q & A: 303 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Manipulation and Software Literacy19%- Software literacy and development tools
  • 1. Python, NumPy, pandas, Jupyter proficiency
  • 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
- Distributed computing with Dask
  • 1. Scaling data operations across multiple GPUs
  • 2. Dask-cuDF for parallel data processing
- GPU-accelerated data manipulation using cuDF
  • 1. Groupby, apply, and aggregation operations
  • 2. cuDF vs pandas API mapping and usage
  • 3. Data integration, joining, merging, and filtering
Topic 2: Machine Learning15%- 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. Hyperparameter tuning techniques
  • 2. Batching and memory-efficient training methods
  • 3. Feature engineering for ML models
- Deep learning frameworks integration
  • 1. Overfitting vs underfitting concepts
  • 2. Using RAPIDS with TensorFlow and PyTorch
Topic 3: GPU and Cloud Computing16%- 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 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
Topic 4: MLOps19%- Model monitoring and management
  • 1. Managing model artifacts and configurations for reproducibility
  • 2. Monitoring production models for drift and performance degradation
- Experiment tracking
  • 1. MLflow, Weights & Biases, and custom tracking tools
  • 2. Benchmarking workflows and selecting optimal hardware
- Model deployment and serving
  • 1. Model saving, loading, and prediction generation
  • 2. Production deployment strategies
- Containerization and environment management
  • 1. Conda environment management
  • 2. Docker for reproducible GPU-accelerated workflows
Topic 5: Data Preparation17%- Data cleaning and quality handling
  • 1. Handling missing values and data quality issues
  • 2. Data governance and compliance
- GPU-accelerated ETL workflows
  • 1. RAPIDS-based ETL pipelines
  • 2. Efficient processing and storage with Parquet
- Feature engineering
  • 1. Feature engineering for numerical and categorical variables
  • 2. Dimensionality reduction and data sampling
- Data loading and preprocessing
  • 1. Handling class imbalance and generating synthetic data
  • 2. NVIDIA DALI for high-performance data loading
Topic 6: Data Analysis14%- Visualization
  • 1. Visualizing data using Plotly and Matplotlib
  • 2. Selecting appropriate plots for different analysis goals
- Time-series analysis
  • 1. Time-series data handling and forecasting
  • 2. Anomaly detection in time-series datasets
- Exploratory data analysis
  • 1. Descriptive statistics and summary analysis
  • 2. Performing EDA on GPU-accelerated datasets
- Graph analytics
  • 1. Creating and analyzing graph data using cuGraph
  • 2. Node importance evaluation and network relationship visualization

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question #1

You are tasked with designing an ETL workflow for a large-scale data processing pipeline using NVIDIA technologies. You need to ensure that the extraction, transformation, and loading phases are optimized for performance using hardware acceleration.
Which of the following NVIDIA technologies would be most suitable for accelerating the ETL process?

  • A. CUDA
  • B. TensorFlow
  • C. RAPIDS
  • D. TensorRT
Answer: C
Question #2

A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

  • A. Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
  • B. Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.
  • C. Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.
  • D. Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
Answer: C
Question #3

A data scientist is using RAPIDS cuML to build a predictive model on a large dataset containing numerical and categorical features.
To optimize feature engineering for accelerated GPU processing, which of the following is the best approach?

  • A. Normalize numerical features using min-max scaling implemented with cuDF, leveraging GPU acceleration.
  • B. Convert all categorical variables to string format for efficient processing in cuDF.
  • C. Convert all numerical features to float64 to maximize precision for feature transformation.
  • D. Apply one-hot encoding on high-cardinality categorical features directly in cuDF without any transformation.
Answer: A
Question #4

You are processing a large dataset using NVIDIA Dask-cuDF to distribute GPU-accelerated computation across multiple nodes. Users report inconsistent execution times, with some jobs taking significantly longer than expected.
Which of the following actions would best help diagnose the performance bottleneck?

  • A. Reduce the number of Dask workers to minimize parallel execution overhead.
  • B. Limit GPU memory usage to force more frequent spilling to disk and observe performance differences.
  • C. Switch to using Pandas with Dask to compare execution speed differences.
  • D. Use Dask's dashboard and NVTX markers to analyze task execution times and GPU utilization.
Answer: D
Question #5

You are building a fraud detection system and have a dataset that includes a feature representing transaction amounts. The values range from a few cents to several thousand dollars.
What is the most appropriate data type for this feature?

  • A. Floating-point (with precision to store cents or fractions of a cent).
  • B. Boolean (True for transactions above $100, False otherwise).
  • C. Categorical (grouping transactions into bins such as "low", "medium", "high").
  • D. Integer (rounding all transaction amounts to whole dollars).
Answer: A

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