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Exam Code: NCP-ADS
Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Professional
Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Professional
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Updated: Sep 10, 2026
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| MLOps | 19% | - Deployment and Monitoring
|
| GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Machine Learning | 15% | - Model Development and Optimization
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
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. Connected Components
- C. PageRank
- D. Single Source Shortest Path (SSSP)
Answer: C
You are tasked with profiling a PyTorch-based deep learning model to identify performance bottlenecks using NVIDIA DLProf. Your goal is to analyze kernel execution times and identify operations causing excessive memory consumption.
Which of the following steps is the MOST appropriate sequence for profiling using DLProf?
- A. Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
- B. Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.
- C. Execute the training script under DLProf TensorBoard mode to visualize performance insights, then re-run the model with automatic mixed precision (AMP) to reduce memory usage.
- D. Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.
Answer: A
You are working with a large dataset in NVIDIA RAPIDS cuDF and notice that the data processing pipeline is taking longer than expected.
Which of the following tools or techniques can help you identify and analyze bottlenecks in the pipeline?
- A. Use nvtx.annotate() to label sections of code and analyze performance in Nsight Systems.
- B. Use cuml.preprocessing.StandardScaler() to normalize the dataset before processing.
- C. Enable pandas compatibility mode in cudf to improve performance.
- D. Use cudf.DataFrame.astype(str) to convert numerical values to strings for easier debugging.
Answer: A
A data scientist is working with a dataset of sensor readings (temperature, pressure, vibration) in different scales and units. To ensure all features contribute equally to a machine learning model, the data needs to be standardized.
Which approach is best for standardizing numerical features?
- A. Use Min-Max scaling to transform values into a fixed range (e.g., [0,1] or [-1,1]).
- B. Apply log transformation to all numerical columns to force them into a uniform distribution.
- C. Apply z-score normalization (standardization) to scale values based on mean and standard deviation.
- D. Convert all numerical features to categorical values using binning.
Answer: C
You are working on a large dataset for a machine learning model that will be trained using RAPIDS cuML. The dataset includes categorical, integer, and floating-point features.
Which of the following approaches is the best practice for determining the optimal data type choice for each feature using NVIDIA's RAPIDS cuDF library?
- A. Use float32 instead of float64 for floating-point numbers when possible, and leverage int8, int16, or int32 for categorical and integer data based on their range.
- B. Use float16 for all floating-point data to reduce memory usage and increase GPU processing speed.
- C. Convert all numerical data to float64 for maximum precision in calculations.
- D. Convert categorical variables into int8 to optimize GPU memory usage.
Answer: A
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