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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 15% | - Model Development and Optimization
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| MLOps | 19% | - Deployment and Monitoring
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
Question #1
A machine learning engineer is tasked with optimizing an image classification model on a cloud platform. The engineer must select a GPU-accelerated instance that balances cost and performance while ensuring compatibility with frameworks like TensorFlow and PyTorch.
Which instance configuration is the most appropriate choice?
A. A CPU-only instance with 128 GB of RAM for increased data processing speed.
B. A cloud instance with NVIDIA A100 GPUs and NVLink support.
C. A cloud instance with integrated graphics rather than dedicated NVIDIA GPUs.
D. A single-core CPU instance with high disk I/O throughput for faster data loading.
Question #2
You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?
A. Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution
B. Drop all missing values using .dropna() instead of filling them, as GPU memory is limited
C. Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF
D. Use cuDF's .fillna() method to replace missing values in GPU memory
Question #3
You are working on a large-scale social network analysis project using cuGraph. Your goal is to identify the most influential individuals in the network based on their connectivity and reach.
Which of the following cuGraph algorithms would be the most appropriate choice for this task?
A. Louvain Clustering
B. PageRank
C. Connected Components
D. Single Source Shortest Path (SSSP)
Question #4
When using cuDF from NVIDIA RAPIDS for GPU-accelerated data manipulation, which of the following operations will not efficiently utilize the GPU?
A. Applying a Python lambda function row-wise to a DataFrame.
B. Merging two large datasets on a common column.
C. Performing a group-by operation followed by an aggregation.
D. Filtering a large dataset based on a condition (e.g., column > 1000).
Question #5
You are building a predictive model for retail sales forecasting and need a dataset that includes historical sales transactions, customer demographics, and external economic indicators (e.g., inflation rate, unemployment rate).
Which of the following datasets would be the most appropriate for your model?
A. A dataset with global temperature trends over the past decade
B. A dataset containing transaction history and customer profiles from a retail company
C. A dataset of product reviews and customer sentiments from an e-commerce website
D. A public dataset of annual GDP per country
Solutions:
| Question #1 Correct Answer: B | Question #2 Correct Answer: D | Question #3 Correct Answer: B | Question #4 Correct Answer: A | Question #5 Correct Answer: B |
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