2026 NVIDIA Pass-Sure NCP-ADS New Test Bootcamp

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning	15%	- Distributed training strategies - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning - Model evaluation and validation
Topic 2: MLOps	19%	- Pipeline automation and orchestration - Model deployment and serving - End-to-end workflow management - Monitoring, logging and maintenance
Topic 3: GPU and Cloud Computing	16%	- Resource management and scaling strategies - CRISP-DM and data science methodology - Cloud GPU environments and deployment - GPU architecture and acceleration principles
Topic 4: Data Manipulation and Software Literacy	19%	- GPU-accelerated ETL workflows - Dependency management and containerization - Data processing libraries selection and usage - Performance profiling and optimization tools
Topic 5: Data Analysis	14%	- Time-series analysis and anomaly detection - Distributed and parallel data processing - Data visualization and graph analytics - Exploratory Data Analysis (EDA)
Topic 6: Data Preparation	17%	- Feature engineering and data type optimization - Data cleaning, preprocessing and transformation - Data validation and quality assurance - Workflow monitoring and bottleneck identification

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q11-Q16):

NEW QUESTION # 11
A machine learning engineer is working with a 1 TB dataset stored in Apache Parquet format and wants to analyze the data for patterns before building a model. The engineer is considering various acceleration methods.
Which of the following approaches would be the best choice for efficient analysis?

A. Convert the Parquet file to a Pandas DataFrame and perform analysis using Pandas functions.
B. Read the Parquet file line by line using Python's built-in file handling functions to save memory.
C. Use a GPU-accelerated library such as RAPIDS cuDF to load and process the Parquet file efficiently.
D. Load the dataset into a relational database and query it using simple SQL statements.

Answer: C

NEW QUESTION # 12
You are training a deep learning model on a large dataset and are deciding whether to use a single GPU or multiple GPUs.
Which of the following are true considerations when comparing single-GPU and multi-GPU training setups? (Select two)

A. Multi-GPU training can significantly reduce training time when the dataset is large and the model is computationally intensive.
B. Multi-GPU setups perform better only when the batch size is reduced.
C. Single-GPU training is limited by the VRAM (video memory) on the GPU, so larger models or datasets may require multi-GPU setups.
D. Multi-GPU training requires modifications to the model architecture to make it compatible with parallel processing.
E. Single-GPU training is generally more cost-effective and should be preferred unless scaling is absolutely necessary.

Answer: A,C

NEW QUESTION # 13
A data science team wants to leverage GPU acceleration for detecting anomalies in a massive IoT sensor dataset that is continuously streaming.
Which of the following NVIDIA-supported methods would be the best for handling real-time anomaly detection in a high-throughput environment?

A. Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.
B. Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.
C. Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.
D. Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.

Answer: B

NEW QUESTION # 14
A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?

A. Using only virtual environments (venv) without managing GPU dependencies separately
B. Manually installing each package and its dependencies using pip
C. Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies
D. Using a single system-wide installation of CUDA and forcing all projects to use the same version

Answer: C

NEW QUESTION # 15
You are working with a large dataset containing missing values, and you need to clean and preprocess the data efficiently.
Which of the following methods provides the best performance when handling missing values in a GPU- accelerated EDA workflow using RAPIDS?

A. Convert the dataset to a cuDF DataFrame and use cudf.DataFrame.fillna() to fill missing values.
B. Ignore missing values since GPU acceleration can handle incomplete data without performance degradation.
C. Drop all missing values using df.dropna() in Pandas before using RAPIDS.
D. Use pandas.DataFrame.fillna() on the dataset before converting it to a cuDF DataFrame.

Answer: A

NEW QUESTION # 16
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