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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are preparing a dataset for training a machine learning model using NVIDIA RAPIDS cuML. The dataset contains a feature representing timestamps in nanoseconds.
To optimize GPU performance while ensuring precision, which data type should you choose?

A) int32 - Uses less memory and can store high-precision timestamps efficiently.
B) bool - Provides a highly efficient way to store timestamps as binary values.
C) datetime64[ns] - Optimizes storage and computation for timestamp data in RAPIDS.
D) object - Allows flexibility in storing timestamps as strings for easier parsing.

2. You are deploying a deep learning model on an edge device with 8GB of available RAM. The model's estimated peak memory usage, including model weights, intermediate tensors, and batch data, is 9.5GB.
What is the best course of action to ensure successful deployment while maintaining performance?

A) Reduce the batch size during inference
B) Increase the device's swap space to compensate for insufficient RAM
C) Offload some computation to cloud-based processing
D) Reduce the number of model parameters by removing layers from the architecture

3. 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 Nsight Systems for managing dataset storage and retrieval performance.
B) NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.
C) NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.
D) NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.

4. You are processing large-scale datasets in Dask-cuDF and observe that your computation involves excessive data shuffling, which slows down performance. You decide to implement data caching to reduce shuffle overhead.
Which of the following best describes a technique to reduce shuffle costs in a Dask-cuDF workflow?

A) Persisting intermediate results in distributed GPU memory using df.persist() ensures that Dask does not recompute partitions, reducing shuffle overhead.
B) Splitting the DataFrame into multiple smaller DataFrames and recomputing each separately reduces shuffle operations in Dask-cuDF.
C) Using .compute() on each partition immediately forces Dask to materialize results and store them in memory, ensuring shuffle operations are avoided.
D) Disabling lazy execution using dask.config.set(lazy=False) forces all computations to execute eagerly, thereby avoiding shuffle.

5. Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

A) Data Understanding
B) Business Understanding
C) Data Preparation
D) Model Building

Solutions: