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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You need to process a dataset containing 10 billion rows, applying complex transformations and aggregations. The dataset does not fit into RAM, and you need an efficient, scalable solution for parallel processing.
Which of the following is the best choice?

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

2. You are building a real-time recommendation system that processes high-frequency transactional data from millions of users.
The system must:
- Ingest and preprocess data efficiently
- Perform similarity computations for user-item recommendations
- Scale to handle rapid incoming transactions
Which of the following NVIDIA technologies is the best choice for this use case?

A) NVIDIA NVTabular
B) NVIDIA Triton Inference Server
C) RAPIDS cuGraph
D) CUDA Kernels with Custom C++ Code

3. You are working on an MLOps project where GPU-accelerated workflows are being used for model training. You want to benchmark and optimize these workflows to ensure the best performance.
Which of the following steps should you consider to effectively benchmark and optimize GPU- accelerated workflows? (Select two)

A) Use a dynamic batch size strategy that adjusts the batch size based on available GPU memory to maximize throughput.
B) Optimize data loading by using data augmentation techniques during training to reduce the time spent on I/O operations.
C) Increase the batch size and learning rate simultaneously to maximize GPU usage and reduce training time.
D) Use profiling tools to measure the GPU utilization and memory usage during training to identify performance bottlenecks.

4. When performing benchmarking and optimization for GPU-accelerated workflows, which of the following tools is best suited for analyzing the memory utilization and computational efficiency of deep learning models running on Nvidia GPUs?

A) Nvidia TensorRT
B) Nvidia CUDA Profiler
C) Nvidia Riva
D) Nvidia Nsight Compute

5. You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?

A) Perform all EDA using NumPy and SciPy for optimized array computations.
B) Use Pandas directly for data manipulation and visualization.
C) Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU.
D) Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.

Solutions: