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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A machine learning engineer runs NVIDIA DLProf to analyze the performance of a deep learning model and receives a report indicating high GPU idle time.
What is the most likely cause of this issue?

A) The GPU is not powerful enough to process the deep learning model efficiently.
B) The batch size is too large, leading to excessive GPU memory utilization and slow processing.
C) The CUDA cores are overheating, leading to automatic throttling of computations.
D) The model is experiencing data loading bottlenecks, causing the GPU to wait for input batches.

2. When comparing the required memory with the available memory on a GPU for an MLOps deployment using NVIDIA technologies, which of the following is the best method to optimize memory usage while training large models?

A) Use mixed-precision training to reduce memory requirements by using half-precision floating-point numbers.
B) Use a higher number of GPUs to distribute the model and memory load across the GPUs.
C) Decrease the number of training iterations to reduce memory consumption.
D) Increase the input data size to fully utilize available memory and improve training performance.

3. You are working with a large dataset containing millions of rows, and you need to store it efficiently for fast read and write operations while maintaining compatibility with CuDF and pandas.
Which of the following file formats is the best choice for efficient columnar storage and GPU-accelerated processing?

A) JSON
B) CSV
C) TXT
D) Parquet

4. You are working on a data science project that requires processing a large-scale dataset stored in CSV format. The dataset contains hundreds of millions of rows, and you want to load it efficiently into NVIDIA RAPIDS cuDF for accelerated processing on a GPU.
Which of the following approaches is the most optimal way to load the dataset?

A) import dask_cudf 2. df = dask_cudf.read_csv("large_dataset.csv")
B) import cudf 2. df = cudf.read_csv("large_dataset.csv", chunksize=100000)
C) import pandas as pd 2. df = pd.read_csv("large_dataset.csv")
D) import cudf 2. df = cudf.DataFrame.from_pandas(pd.read_csv("large_dataset.csv"))

5. After profiling a deep learning model using NVIDIA DLProf, you notice that a specific GEMM (General Matrix Multiplication) operation takes significantly longer than expected. The profiler output reveals that tensor cores are underutilized despite having an Ampere-based GPU with Tensor Cores enabled.
Which of the following actions is the MOST appropriate to improve performance?

A) Convert the model's data type to float16 or bfloat16 and re-run the training with automatic mixed precision (AMP).
B) Increase the batch size to maximize GPU memory usage and reduce kernel launch overhead.
C) Disable CUDA graphs and enforce PyTorch's eager execution mode to improve kernel execution order.
D) Switch from stochastic gradient descent (SGD) to Adam optimizer, as Adam improves convergence and computational efficiency.

Solutions: