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 |
|---|---|---|
| Topic 1: GPU and Cloud Computing | 16% | - Cloud GPU environments and deployment - Resource management and scaling strategies - GPU architecture and acceleration principles - CRISP-DM and data science methodology |
| Topic 2: MLOps | 19% | - Monitoring, logging and maintenance - Pipeline automation and orchestration - Model deployment and serving - End-to-end workflow management |
| Topic 3: Data Manipulation and Software Literacy | 19% | - Data processing libraries selection and usage - Performance profiling and optimization tools - GPU-accelerated ETL workflows - Dependency management and containerization |
| Topic 4: Machine Learning | 15% | - Distributed training strategies - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms - Model evaluation and validation |
| Topic 5: Data Analysis | 14% | - Time-series analysis and anomaly detection - Data visualization and graph analytics - Exploratory Data Analysis (EDA) - Distributed and parallel data processing |
| Topic 6: Data Preparation | 17% | - Feature engineering and data type optimization - Data validation and quality assurance - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are designing a data pipeline for a healthcare analytics company that processes petabytes of structured and semi-structured patient data. Your goal is to optimize performance while maintaining accuracy.
Which of the following strategies is the most appropriate choice for accelerating this workload?
- A. Load the data into a PostgreSQL database and run SQL queries for all transformations before training a model.
- B. Use RAPIDS cuDF and Dask-cuDF for distributed preprocessing and RAPIDS cuML for GPU- accelerated machine learning model training.
- C. Use RAPIDS cuDF for data ingestion, preprocessing, and transformation, then offload model training to scikit-learn on CPUs.
- D. Use Apache Hadoop and MapReduce for preprocessing before training models on a single GPU.
Correct Answer: B 🗳️
A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?
- A. Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.
- B. Rely on Matplotlib to generate static plots and update them every minute with a loop.
- C. Precompute the time-series visualization with Dask and display it in a static HTML page.
- D. Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.
Correct Answer: D 🗳️
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 Magnum IO for high-performance I/O and dataset storage optimization.
- B. NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.
- C. NVIDIA Nsight Systems for managing dataset storage and retrieval performance.
- D. NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.
Correct Answer: A 🗳️
You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?
- A. Use Pandas directly for data manipulation and visualization.
- B. Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU. - C. Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.
- D. Perform all EDA using NumPy and SciPy for optimized array computations.
Correct Answer: B 🗳️
A research team is analyzing a large transportation network and wants to identify groups of locations that are closely connected based on travel routes.
Which NVIDIA cuGraph function would be the most effective for this task?
- A. Use cuGraph's pagerank() to rank the most connected locations.
- B. Use cuGraph's louvain() function to detect communities within the graph.
- C. Use cuGraph's shortest_path() to compute all shortest paths and derive clusters manually.
- D. Apply cuGraph's bfs() (Breadth-First Search) to find clusters of locations.
Correct Answer: B 🗳️
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