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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation | 17% | - Data validation and quality assurance - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization - Workflow monitoring and bottleneck identification |
| Topic 2: Data Analysis | 14% | - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA) - Distributed and parallel data processing - Data visualization and graph analytics |
| Topic 3: Data Manipulation and Software Literacy | 19% | - GPU-accelerated ETL workflows - Performance profiling and optimization tools - Data processing libraries selection and usage - Dependency management and containerization |
| Topic 4: Machine Learning | 15% | - GPU-accelerated ML frameworks and algorithms - Model evaluation and validation - Model training and hyperparameter tuning - Distributed training strategies |
| Topic 5: MLOps | 19% | - End-to-end workflow management - Pipeline automation and orchestration - Monitoring, logging and maintenance - Model deployment and serving |
| Topic 6: GPU and Cloud Computing | 16% | - Resource management and scaling strategies - CRISP-DM and data science methodology - Cloud GPU environments and deployment - GPU architecture and acceleration principles |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. A data scientist is working on a dataset where the numerical features have different ranges, and they need to ensure uniformity across features before training a machine learning model.
Which of the following approaches, utilizing NVIDIA technologies, would best achieve this goal?
A) Use cuML's StandardScaler() to transform the features to have zero mean and unit variance.
B) Use cuML's PCA to directly remove the need for standardization by reducing dimensionality.
C) Apply cuDF's normalize() function to scale each feature between 0 and 1.
D) Apply cuML's RobustScaler() to center the data using median and scale using the interquartile range.
2. You are building a predictive model for retail sales forecasting and need a dataset that includes historical sales transactions, customer demographics, and external economic indicators (e.g., inflation rate, unemployment rate).
Which of the following datasets would be the most appropriate for your model?
A) A dataset with global temperature trends over the past decade
B) A dataset containing transaction history and customer profiles from a retail company
C) A public dataset of annual GDP per country
D) A dataset of product reviews and customer sentiments from an e-commerce website
3. A data scientist wants to compare the performance of two different GPU-accelerated data science frameworks, NVIDIA RAPIDS (cuDF, cuML) and TensorFlow, for a tabular data classification task.
Which of the following approaches would be the best practice for designing an unbiased and effective benchmark?
A) Run all benchmarks on a CPU to ensure fairness across frameworks.
B) Measure execution time and memory usage for each framework using NVIDIA Nsight Systems (nsys).
C) Use TensorFlow's built-in training time metrics without comparing equivalent RAPIDS-based operations.
D) Ignore preprocessing and focus only on model training speed when comparing performance.
4. You are working on a data science project that requires processing a large-scale dataset stored in CSV format. The dataset contains hundreds of millions of rows, and you want to load it efficiently into NVIDIA RAPIDS cuDF for accelerated processing on a GPU.
Which of the following approaches is the most optimal way to load the dataset?
A) import cudf 2. df = cudf.read_csv("large_dataset.csv", chunksize=100000)
B) import cudf 2. df = cudf.DataFrame.from_pandas(pd.read_csv("large_dataset.csv"))
C) import dask_cudf 2. df = dask_cudf.read_csv("large_dataset.csv")
D) import pandas as pd 2. df = pd.read_csv("large_dataset.csv")
5. You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?
A) Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
B) Use Dask DataFrame to parallelize deep learning model training across multiple GPUs
C) Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution
D) Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: A |
