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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 15% | - Distributed training strategies - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning - Model evaluation and validation |
| MLOps | 19% | - End-to-end workflow management - Pipeline automation and orchestration - Model deployment and serving - Monitoring, logging and maintenance |
| Data Preparation | 17% | - Data cleaning, preprocessing and transformation - Data validation and quality assurance - Feature engineering and data type optimization - Workflow monitoring and bottleneck identification |
| Data Manipulation and Software Literacy | 19% | - Data processing libraries selection and usage - Performance profiling and optimization tools - Dependency management and containerization - GPU-accelerated ETL workflows |
| Data Analysis | 14% | - Time-series analysis and anomaly detection - Data visualization and graph analytics - Exploratory Data Analysis (EDA) - Distributed and parallel data processing |
| 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 |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. You are working on a data science project that involves processing large-scale financial transaction data. You want to optimize data manipulation operations using NVIDIA's RAPIDS cuDF.
Which of the following approaches best leverages NVIDIA technologies for efficient data manipulation?
A) Preprocess the data using Apache Spark's CPU-based DataFrame API before transferring it to a GPU for machine learning.
B) Load the dataset into a Pandas DataFrame and use multi-threading to speed up operations.
C) Use cuDF DataFrames for data manipulation and rely on GPU-accelerated functions like .groupby(),
.merge(), and .applymap().
D) Convert the dataset into a SQLite database and execute SQL queries to perform data transformations.
2. A data scientist wants to process a large dataset using multiple GPUs on an NVIDIA-supported system. They decide to use Dask to enable parallelism.
Which of the following steps is most essential for leveraging Dask for multi-GPU scaling?
A) Use dask.array or dask.dataframe with dask_cuda.CUDACluster to distribute computations across multiple GPUs
B) Use ThreadPoolExecutor to manage parallel computations on GPUs
C) Train a deep learning model on a single GPU first, then switch to Dask for scaling
D) Convert all data into Pandas DataFrames before distributing computations
3. Which of the following techniques are best suited for efficiently processing and organizing large datasets using NVIDIA technologies? (Select two)
A) Using cuDF for GPU-accelerated DataFrame operations
B) Leveraging Dask for distributed GPU-based parallel computing
C) Using TensorFlow with custom data pipelines for data loading
D) Storing data directly on an HDD and processing with GPUs
4. Which of the following scenarios are most appropriate for using GPU acceleration when working with large-scale datasets in machine learning? (Select two)
A) Performing exploratory data analysis (EDA) on a dataset of 100,000 rows with 10 features.
B) Using decision trees for a classification task with a dataset of 1 million rows and 20 features.
C) Running a deep learning model for image classification with millions of labeled images.
D) Running a large ensemble of simple models (e.g., random forests) on a dataset of 10 million rows.
E) Training a large-scale natural language processing (NLP) model on text data with billions of words.
5. A team is processing large-scale tabular data using cuDF and cuML on NVIDIA GPUs but is facing performance degradation.
Which of the following techniques would be the most effective in identifying and resolving bottlenecks in the pipeline?
A) Use nvprof or nsight compute to analyze kernel execution time and memory transfer efficiency.
B) Increase GPU clock speed manually to force higher processing power.
C) Convert all datasets into pandas DataFrames for initial processing before moving to cuDF.
D) Reduce the dataset size to a smaller sample to speed up processing.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: A,B | Question # 4 Answer: C,E | Question # 5 Answer: A |
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