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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: GPU and Cloud Computing | 16% | - GPU architecture and acceleration principles - Cloud GPU environments and deployment - CRISP-DM and data science methodology - Resource management and scaling strategies |
| Topic 2: Data Preparation | 17% | - Data validation and quality assurance - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization - Data cleaning, preprocessing and transformation |
| Topic 3: Machine Learning | 15% | - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms - Model evaluation and validation - Distributed training strategies |
| Topic 4: Data Analysis | 14% | - Data visualization and graph analytics - Exploratory Data Analysis (EDA) - Time-series analysis and anomaly detection - Distributed and parallel data processing |
| Topic 5: Data Manipulation and Software Literacy | 19% | - Performance profiling and optimization tools - Dependency management and containerization - GPU-accelerated ETL workflows - Data processing libraries selection and usage |
| Topic 6: MLOps | 19% | - Model deployment and serving - End-to-end workflow management - Monitoring, logging and maintenance - Pipeline automation and orchestration |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are working with a dataset containing hundreds of millions of records, and you need to perform ETL operations such as filtering, joins, and aggregations. Given the dataset size, which NVIDIA- accelerated library should you use to achieve optimal performance?
- A. NumPy, because it is optimized for numerical computing and offers better performance for handling tabular data.
- B. cuDF, as it provides GPU-accelerated DataFrame operations similar to Pandas, allowing for efficient processing of large datasets.
- C. cuPy, because it provides GPU-accelerated array operations, making it the best option for processing tabular data.
- D. Pandas, as it is widely used and supports all common DataFrame operations, even for very large datasets.
Correct Answer: B 🗳️
In a typical MLOps pipeline, which of the following practices are essential to ensuring robust deployment and monitoring of machine learning models in production? (Select two)
- A. Automated hyperparameter tuning during inference to optimize model performance.
- B. Use of manual intervention for every model update to ensure accuracy.
- C. Post-deployment data drift detection to assess model performance degradation.
- D. Continuous integration and continuous deployment (CI/CD) pipelines for model updates.
Correct Answer: C,D 🗳️
Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)
- A. Using Apache Spark for distributed data processing
- B. Using SQLite as a local database for large-scale data analysis
- C. Using Excel to manipulate large datasets
- D. Using SQLAlchemy to interact with databases for large data processing
- E. Using Dask for parallel processing of large datasets
Correct Answer: A,E 🗳️
You are designing a reproducible benchmark to compare the performance of deep learning models across frameworks like PyTorch and TensorFlow using NVIDIA's A100 GPU.
Which step is most critical in ensuring fair benchmarking conditions?
- A. Using a different precision setting for each framework to maximize performance per framework's capabilities.
- B. Measuring only forward pass latency to compare inference speed while ignoring backward pass computation.
- C. Ensuring the same CUDA/cuDNN and driver versions are installed when running benchmarks across frameworks.
- D. Enabling XLA compiler optimizations only for TensorFlow to enhance its performance.
Correct Answer: C 🗳️
You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?
- A. RAPIDS + Dask for distributed GPU-accelerated ETL
- B. Apache Spark with its default CPU-based execution
- C. Pandas with multiprocessing
- D. Hadoop MapReduce
Correct Answer: A 🗳️
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