NCP-ADS Latest Exam Registration | NCP-ADS Valid Mock Exam

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q284-Q289):

NEW QUESTION # 284
When deciding whether to use GPU acceleration or a traditional CPU approach for a machine learning task, which of the following factors should be considered to determine if the data qualifies as "big data" and whether GPU acceleration is beneficial? (Select two)

A. The size of the dataset in terms of rows and columns is irrelevant when determining if it qualifies as big data.
B. The dataset must be over 100GB in size to qualify as big data and warrant GPU acceleration.
C. GPU acceleration is beneficial when the dataset can be divided into independent chunks that can be processed in parallel.
D. CPU-based machine learning methods are always more effective for small datasets, regardless of the algorithm used.
E. The complexity of the algorithm being used plays a crucial role in deciding whether to use GPU acceleration, with more complex algorithms benefiting from parallel computation.

Answer: C,E

NEW QUESTION # 285
You are working on a large-scale graph analysis project using NVIDIA cuGraph for accelerated computations. Your dataset consists of millions of nodes and edges representing social network interactions. You need to efficiently compute PageRank while minimizing memory usage.
Which of the following techniques would be the most effective?

A. Manually unroll loops and precompute node ranks in a NumPy array before feeding them into cuGraph.
B. Convert the graph into an adjacency list format and store it in Python dictionaries for faster lookups.
C. Use cuGraph's sparse matrix representation to store graph data and perform computations efficiently.
D. Use Pandas DataFrames to manage the graph structure and leverage Pandas indexing for efficient queries.

Answer: C

NEW QUESTION # 286
Which of the following Nvidia technologies is commonly used for deploying machine learning models in production environments, enabling scalable deployment and monitoring?

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

Answer: C

NEW QUESTION # 287
A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A. Reducing dataset size by randomly removing data points without considering class balance.
B. Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.
C. Using a single-threaded feature extraction approach to avoid overhead from parallelization.
D. Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.

Answer: D

NEW QUESTION # 288
You are working with cloud-based GPUs to process a large dataset (terabytes in size) stored in Parquet format. One column represents a unique identifier (e.g., product ID), and it contains only positive integers ranging from 1 to 100,000.
Which of the following data types provides the best balance of memory efficiency and performance?

A. float64
B. int8
C. float32
D. uint16

Answer: D

NEW QUESTION # 289
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