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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are building a fraud detection system and have a dataset that includes a feature representing transaction amounts. The values range from a few cents to several thousand dollars.
What is the most appropriate data type for this feature?

A) Floating-point (with precision to store cents or fractions of a cent).
B) Categorical (grouping transactions into bins such as "low", "medium", "high").
C) Integer (rounding all transaction amounts to whole dollars).
D) Boolean (True for transactions above $100, False otherwise).

2. A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?

A) Running training exclusively on CPU
B) Disabling data parallelism
C) Using mixed-precision training with Tensor Cores
D) Prefetching data with the CPU while training on the GPU

3. You are working on a medium-sized dataset (~500,000 rows, 20 columns) and need to perform fast exploratory data analysis (EDA) with filtering, aggregations, and transformations.
Which of the following Python libraries would be the most efficient choice for this task?

A) Vaex
B) PySpark
C) Pandas
D) Dask

4. Which of the following methods are commonly used to handle missing data in data analysis? (Select two)

A) Removing rows with missing data
B) Using the mode to impute missing values
C) Using a fixed constant value for imputation
D) Imputing with the mean or median of the feature
E) Ignoring missing data entirely

5. You are a data scientist working on a large-scale deep learning project. Your team needs to train a deep neural network (DNN) on terabytes of image data using NVIDIA GPUs in the cloud.
Which of the following approaches will maximize GPU utilization and optimize training time in an NVIDIA- accelerated cloud environment?

A) Avoid using mixed precision training and rely solely on full-precision (FP32) computations for numerical stability.
B) Select low-cost, consumer-grade GPUs available in the cloud marketplace to reduce costs.
C) Use Jupyter Notebooks on a local machine with minimal GPU resources and transfer model checkpoints manually to the cloud for training.
D) Use NVIDIA NGC containers with TensorFlow or PyTorch and enable NCCL for multi-GPU communication.

Solutions: