Dumps NVIDIA NCP-ADS Download & NCP-ADS Real Exam Questions
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NCP-ADS Real Exam Questions & NCP-ADS Test Dump
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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q31-Q36):
NEW QUESTION # 31
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. Using a single-threaded feature extraction approach to avoid overhead from parallelization.
- B. Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.
- C. Reducing dataset size by randomly removing data points without considering class balance.
- D. Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.
Answer: B
NEW QUESTION # 32
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 Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.
- B. Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
- 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.
Answer: D
NEW QUESTION # 33
You are analyzing a large-scale transportation network using cuGraph and notice that query times are longer than expected when running graph algorithms.
What is the best way to optimize graph processing performance using GPU-accelerated tools?
- A. Use cugraph.to_directed() to convert the graph into a directed format, which improves GPU parallelism.
- B. Store the graph in COO (Coordinate List) format instead of CSR (Compressed Sparse Row) format for faster traversal.
- C. Use cugraph.filter_unconnected_nodes() to remove unconnected nodes before processing.
- D. Convert the graph to CSR (Compressed Sparse Row) format before running computations to improve memory efficiency.
Answer: D
NEW QUESTION # 34
You are building a real-time recommendation system that processes high-frequency transactional data from millions of users.
The system must:
- Ingest and preprocess data efficiently
- Perform similarity computations for user-item recommendations
- Scale to handle rapid incoming transactions
Which of the following NVIDIA technologies is the best choice for this use case?
- A. NVIDIA Triton Inference Server
- B. CUDA Kernels with Custom C++ Code
- C. NVIDIA NVTabular
- D. RAPIDS cuGraph
Answer: C
NEW QUESTION # 35
Which of the following techniques are commonly used to identify and acquire datasets for machine learning and data science projects? (Select three)
- A. Data augmentation
- B. Public APIs
- C. Crowdsourcing
- D. Web scraping
- E. Manual data entry
Answer: B,C,D
NEW QUESTION # 36
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