Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Professional
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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
|
| GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are working on a data science project using NVIDIA RAPIDS on a multi-GPU system.
To ensure reproducibility and avoid software versioning conflicts, which of the following is the best approach for managing dependencies?
- A. Avoid dependency management frameworks and rely on manual tracking of package versions using a text file.
- B. Use a Conda environment with RAPIDS-compatible versions of libraries installed using conda install
-c rapidsai -c nvidia. - C. Install all required packages globally on the system using pip install without a virtual environment.
- D. Use a manually compiled CUDA installation alongside system-installed Python libraries to manage GPU dependencies.
Correct Answer: B 🗳️
You are working with a data science project that requires GPU acceleration for machine learning tasks. Your team is facing challenges with software version conflicts between different dependencies when deploying the project on different systems.
Which of the following solutions should you consider to efficiently manage software dependencies and avoid conflicts? (Select two)
- A. Manually install all dependencies directly on the host machine to avoid using dependency management tools.
- B. Install GPU drivers on the host machine and rely on the local system environment for dependency management.
- C. Use Docker to containerize the project, ensuring that the dependencies and environment are consistent across different systems.
- D. Set up a virtual machine for each different dependency configuration to isolate environments.
- E. Use Conda to create isolated environments for different versions of dependencies, ensuring version compatibility.
Correct Answer: C,E 🗳️
You are tasked with designing and implementing a benchmark to compare the performance of different deep learning frameworks, including TensorFlow, PyTorch, and JAX, using NVIDIA GPUs.
Which of the following is the most effective approach to ensure an accurate and fair comparison?
- A. Ensure identical hardware configurations, dataset preprocessing, and model architectures while leveraging NVIDIA's Nsight Systems and DLProf for analysis.
- B. Compare training times only without considering throughput, power efficiency, or memory utilization.
- C. Run each framework with default settings to compare their out-of-the-box performance without any optimizations.
- D. Use mixed precision (FP16) training only in TensorFlow to maximize performance while keeping other frameworks at FP32.
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 RAPIDS cuDF to leverage GPU acceleration for large-scale DataFrame operations.
- B. NVIDIA TensorRT to optimize DataFrame transformations and aggregations using deep learning.
- C. NVIDIA Nsight Compute to profile and optimize the performance of GPU-based aggregations.
- D. NVIDIA Triton Inference Server to accelerate data processing workflows on a single GPU.
Correct Answer: A 🗳️
You are working on a dataset containing missing values, duplicate records, and inconsistent data types.
The dataset size is 15GB and you need to efficiently perform data cleansing operations such as:
- Handling missing values
- Dropping duplicates
- Converting data types
Which of the following approaches would be the most efficient way to perform these operations on an NVIDIA GPU?
- A. Use pandas to load the dataset and perform the operations using standard pandas functions
- B. Use Vaex to perform data cleansing, as it is optimized for large-scale datasets
- C. Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame
- D. Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods
Correct Answer: D 🗳️
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