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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. 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 containing transaction history and customer profiles from a retail company
B) A public dataset of annual GDP per country
C) A dataset of product reviews and customer sentiments from an e-commerce website
D) A dataset with global temperature trends over the past decade

2. You are optimizing a deep learning model that runs on an NVIDIA GPU and notice that inference latency is unexpectedly high. You decide to use DLProf to analyze the model's execution profile. After running the profiler, you find that a significant portion of execution time is spent on a single GPU kernel.
Which of the following actions would best help you identify and optimize this performance bottleneck?

A) Reduce the batch size to minimize the time spent on memory-bound operations and improve kernel efficiency.
B) Use DLProf's Tensor Core Analysis feature to determine if Tensor Cores are being utilized effectively.
C) Modify the neural network architecture to use more convolutional layers, as this generally improves execution speed on NVIDIA GPUs.
D) Switch to a CPU-based execution environment, as it will eliminate any potential GPU bottlenecks.

3. A data scientist needs to process a dataset containing 10 million records, performing transformations and exploratory data analysis (EDA). The processing needs to be efficient but does not require high- performance multi-GPU execution.
Which of the following libraries provides the best balance between usability and performance?

A) Dask DataFrame, since it automatically parallelizes computations even when the dataset fits in memory.
B) Pandas, as it provides a simple API and works well for datasets that fit within system memory.
C) cuDF, since GPU acceleration will still provide a speedup even for moderately sized datasets.
D) Spark DataFrame, as it is optimized for distributed processing and scales well even for 10 million records.

4. You are tasked with implementing a multi-GPU data pipeline using Dask-CUDA to process large datasets stored in Parquet format. Your goal is to achieve optimal GPU memory utilization and minimize inter-GPU communication overhead.
Which of the following approaches best aligns with these goals?

A) Use dask_cudf.read_parquet() with split_row_groups=True to evenly distribute data across GPUs.
B) Use dask.persist() instead of dask.compute() to force immediate execution of tasks before distribution to GPUs.
C) Use dask.array instead of dask_cudf because it provides better performance for structured tabular data.
D) Set dask.config.set({'distributed.worker.memory.target': 0.9}) to allocate 90% of the total CPU memory for GPU operations.

5. You are working on a large dataset (several terabytes in size) and need to perform data preprocessing, filtering, and transformations before training a machine learning model.
Given the dataset size and the requirement to optimize for GPU acceleration using NVIDIA technologies, which of the following is the most appropriate data processing library to use?

A) Dask DataFrame with Dask-CUDA
B) Modin with Ray backend
C) NumPy with CuPy acceleration
D) pandas

Solutions: