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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

You are training a deep learning model on a large dataset and are deciding whether to use a single GPU or multiple GPUs.
Which of the following are true considerations when comparing single-GPU and multi-GPU training setups? (Select two)

A. Single-GPU training is generally more cost-effective and should be preferred unless scaling is absolutely necessary.
B. Multi-GPU training requires modifications to the model architecture to make it compatible with parallel processing.
C. Multi-GPU setups perform better only when the batch size is reduced.
D. Single-GPU training is limited by the VRAM (video memory) on the GPU, so larger models or datasets may require multi-GPU setups.
E. Multi-GPU training can significantly reduce training time when the dataset is large and the model is computationally intensive.

Question 2

A data scientist is working on training a deep learning model in a cloud-based environment. The dataset is large, and model convergence is taking too long on a standard CPU instance.
To optimize performance through GPU acceleration, which of the following strategies should the data scientist implement?

A. Store all training data in RAM and load it directly to the CPU for processing.
B. Disable CUDA and use only OpenMP to parallelize computations across CPU cores.
C. Use a cloud instance with multiple GPUs and enable mixed-precision training.
D. Increase the number of CPU cores and distribute training across multiple CPU threads.

Question 3

A data engineer is tasked with processing a 5 TB dataset stored in Apache Parquet format. The dataset consists of user activity logs and needs to be filtered, aggregated, and processed for feature engineering before training an ML model. The engineer is deciding between Dask and Apache Spark.
Which statement best describes a key difference between the two frameworks?

A. Dask is optimized for in-memory processing, while Spark requires disk-based storage for computation.
B. Spark is better suited for structured and semi-structured data, while Dask excels at unstructured data processing.
C. Dask is a more lightweight solution, often preferred for Python-centric workflows, whereas Spark provides broader ecosystem integrations and supports SQL-like operations natively.
D. Spark cannot utilize GPUs, whereas Dask has built-in GPU acceleration by default.

Question 4

You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A. Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively
B. Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib
C. Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly
D. Load the data into a Spark DataFrame and visualize using Apache Zeppelin

Question 5

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. Storing data directly on an HDD and processing with GPUs
D. Using TensorFlow with custom data pipelines for data loading

Solutions: