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NVIDIA NCP-ADS dumps - in .pdf
- Exam Code: NCP-ADS
- Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
- Updated: Sep 16, 2026
- Q & A: 303 Questions and Answers
- PDF Price: $59.99
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- Exam Code: NCP-ADS
- Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
- Updated: Sep 16, 2026
- Q & A: 303 Questions and Answers
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- Exam Code: NCP-ADS
- Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
- Updated: Sep 16, 2026
- Q & A: 303 Questions and Answers
- Software Price: $59.99
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis | 14% | - Exploratory data analysis
|
| Topic 2: MLOps | 19% | - Model monitoring and management
|
| Topic 3: Machine Learning | 15% | - Deep learning frameworks integration
|
| Topic 4: Data Preparation | 17% | - Data cleaning and quality handling
|
| Topic 5: Data Manipulation and Software Literacy | 19% | - Distributed computing with Dask
|
| Topic 6: GPU and Cloud Computing | 16% | - GPU resource management
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
Question #1
You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?
- A. NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.
- B. NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.
- C. NVIDIA Nsight Systems for managing dataset storage and retrieval performance.
- D. NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.
Correct Answer: D 🗳️
Question #2
You are analyzing a transportation network where airports represent nodes and flight routes represent edges. You need to determine the most critical airports in the network based on how many shortest paths pass through them.
Which cuGraph centrality algorithm should you use for this task?
- A. Degree Centrality, as it measures the number of direct connections an airport has, indicating its importance in the network.
- B. Triangle Count, as it determines the number of three-node cycles an airport is part of, indicating its importance in network structure.
- C. Closeness Centrality, as it measures how close an airport is to all other airports, making it the best indicator of global connectivity.
- D. Betweenness Centrality, as it identifies airports that act as major transit hubs by measuring how frequently they appear in shortest paths.
Correct Answer: D 🗳️
Question #3
You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.
Which approach best utilizes NVIDIA technologies for efficient forecasting?
- A. Use PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.
- B. Use cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.
- C. Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.
- D. Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
Correct Answer: B 🗳️
Question #4
You are working on a data science project that requires processing a large-scale dataset stored in CSV format. The dataset contains hundreds of millions of rows, and you want to load it efficiently into NVIDIA RAPIDS cuDF for accelerated processing on a GPU.
Which of the following approaches is the most optimal way to load the dataset?
- A. import cudf 2. df = cudf.DataFrame.from_pandas(pd.read_csv("large_dataset.csv"))
- B. import cudf 2. df = cudf.read_csv("large_dataset.csv", chunksize=100000)
- C. import pandas as pd 2. df = pd.read_csv("large_dataset.csv")
- D. import dask_cudf 2. df = dask_cudf.read_csv("large_dataset.csv")
Correct Answer: D 🗳️
Question #5
You are working on a data science project that requires augmenting a dataset using synthetic data.
You are utilizing cuDF and NVIDIA RAPIDS to speed up the data generation process.
Which of the following methods is the most effective way to generate synthetic data using cuDF in a RAPIDS workflow?
- A. Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.
- B. Use cudf.DataFrame.applymap() to create new synthetic features through complex mathematical functions.
- C. Use cuDF to manipulate data distributions and generate new data points based on existing features.
Correct Answer: C 🗳️
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