NVIDIA NCP-ADS Exam Dumps - Pass Your Exam In First Attempt [2026]

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
MLOps	19%	- Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - Model deployment and serving 1. Production deployment strategies 2. Model saving, loading, and prediction generation - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management
GPU and Cloud Computing	16%	- Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - Performance optimization 1. Single and multi-GPU performance optimization 2. Mixed precision and bottleneck analysis 3. Memory profiling with DLProf - 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
Machine Learning	15%	- Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Feature engineering for ML models 3. Batching and memory-efficient training methods - 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 - Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts
Data Analysis	14%	- Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets
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. cuDF vs pandas API mapping and usage 2. Groupby, apply, and aggregation operations 3. Data integration, joining, merging, and filtering - Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing
Data Preparation	17%	- Data loading and preprocessing 1. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data - 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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q118-Q123):

NEW QUESTION # 118
You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.
Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

A. df.info(memory_usage='deep')
B. df.memory_usage(deep=True).sum()
C. np.array(df).nbytes
D. sys.getsizeof(df)

Answer: B

NEW QUESTION # 119
You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?

A. Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs
B. Use Dask DataFrame to parallelize deep learning model training across multiple GPUs
C. Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
D. Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution

Answer: C

NEW QUESTION # 120
Which of the following is the main advantage of using TensorRT for inference in an accelerated data science pipeline?

A. TensorRT is only compatible with image classification models and does not support other model types.
B. TensorRT optimizes deep learning models to run efficiently on NVIDIA GPUs by reducing precision while maintaining accuracy.
C. TensorRT is mainly used for data visualization and not for model inference.
D. TensorRT automatically builds training models from raw data without requiring pre-trained models.

Answer: B

NEW QUESTION # 121
You need to benchmark GPU-accelerated data science frameworks across both cloud-based and on-premise GPU setups.
Which of the following is the most effective strategy for ensuring consistent and reliable benchmarking results?

A. Running each benchmark multiple times and averaging the results to account for variance in execution time.
B. Using mixed cloud and on-premise GPU instances to test various hardware types and configurations.
C. Running benchmarks in isolated virtual machines to simulate real-world multi-tenant cloud environments.
D. Prioritizing synthetic benchmarks over real-world workloads to get a more controlled performance measurement.

Answer: A

NEW QUESTION # 122
You are working on a machine learning project that requires selecting the optimal data types for each feature in your dataset to maximize performance and efficiency in an MLOps pipeline.
Which of the following data types is most suitable for GPU-accelerated machine learning workflows when working with large datasets on NVIDIA platforms?

A. String
B. Float64
C. Int32
D. Float32

Answer: D

NEW QUESTION # 123
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