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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 15% | - Model Development and Optimization
|
| MLOps | 19% | - Deployment and Monitoring
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
Which of the following is the primary benefit of using NVIDIA RAPIDS in the context of data science workflows?
- A. It simplifies the creation of visualizations for large datasets
- B. It enables real-time data streaming and analytics
- C. It allows for the implementation of reinforcement learning algorithms
- D. It speeds up the preprocessing and analysis of large datasets by utilizing GPUs
Correct Answer: D 🗳️
Which of the following scenarios are most appropriate for using GPU acceleration when working with large-scale datasets in machine learning? (Select two)
- A. Using decision trees for a classification task with a dataset of 1 million rows and 20 features.
- B. Training a large-scale natural language processing (NLP) model on text data with billions of words.
- C. Performing exploratory data analysis (EDA) on a dataset of 100,000 rows with 10 features.
- D. Running a large ensemble of simple models (e.g., random forests) on a dataset of 10 million rows.
- E. Running a deep learning model for image classification with millions of labeled images.
Correct Answer: B,E 🗳️
A data scientist is analyzing a large dataset of financial transactions containing millions of records.
To efficiently perform exploratory data analysis (EDA) using RAPIDS cuDF, which approach provides the most optimized performance while ensuring comprehensive insights?
- A. Use RAPIDS cuDF functions like .describe() and .value_counts() to perform statistical summaries directly on the GPU.
- B. Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.
- C. Downsample the dataset and analyze a subset using Pandas for efficiency.
- D. Perform all analysis on the CPU to avoid potential GPU memory limitations.
Correct Answer: A 🗳️
A machine learning engineer wants to evaluate the performance of NVIDIA RAPIDS cuDF and Apache Spark for large-scale data processing on a GPU-enabled cluster.
Which of the following strategies is the most effective for obtaining a fair and comprehensive benchmark?
- A. Execute identical ETL workflows on cuDF and Spark-RAPIDS and measure execution time and resource utilization.
- B. Limit the benchmark to small datasets since GPUs excel at parallel processing.
- C. Run Spark on a CPU cluster while running RAPIDS on a GPU to compare real-world scenarios.
- D. Focus only on processing speed without considering resource consumption differences between frameworks.
Correct Answer: A 🗳️
You are working with a dataset containing billions of rows and need to perform data transformations, aggregations, and joins efficiently on a single-node GPU-enabled workstation.
Which NVIDIA technology is best suited to optimize performance for these operations?
- A. NVIDIA Nsight Compute to profile and optimize the performance of GPU-based aggregations.
- B. NVIDIA TensorRT to optimize DataFrame transformations and aggregations using deep learning.
- C. NVIDIA RAPIDS cuDF to leverage GPU acceleration for large-scale DataFrame operations.
- D. NVIDIA Triton Inference Server to accelerate data processing workflows on a single GPU.
Correct Answer: C 🗳️
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