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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?

A) df = cudf.to_gpu(pd.read_parquet("data.parquet"))
B) df = cudf.read_csv("data.parquet")
C) df = cudf.read_parquet("data.parquet")
D) df = pd.read_parquet("data.parquet")

3. You have a pandas DataFrame with a column containing floating-point numbers, but it takes up too much memory. You want to convert it into a lower-precision type using CuDF or pandas while ensuring computational efficiency.
Which function would you use?

A) df.convert_dtypes()
B) df.astype('float16')
C) df.to_float16()
D) df['col'].apply(lambda x: np.float16(x))

4. You are tasked with profiling a PyTorch-based deep learning model to identify performance bottlenecks using NVIDIA DLProf. Your goal is to analyze kernel execution times and identify operations causing excessive memory consumption.
Which of the following steps is the MOST appropriate sequence for profiling using DLProf?

A) Execute the training script under DLProf TensorBoard mode to visualize performance insights, then re-run the model with automatic mixed precision (AMP) to reduce memory usage.
B) Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
C) Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.
D) Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.

5. You are setting up a GPU-accelerated data science environment on a cloud-based instance that utilizes NVIDIA GPUs.
To ensure compatibility between CUDA, RAPIDS, and Python libraries, which of the following is the most effective approach for managing dependencies and avoiding version conflicts?

A) Install RAPIDS, PyTorch, TensorFlow, and other libraries in the same Conda base environment for simplified access
B) Manually install required libraries in a system-wide Python environment using pip install without version specifications
C) Use a virtual machine with all dependencies pre-installed to avoid any software conflicts
D) Use Conda environments with version-pinned dependencies to create an isolated environment for RAPIDS and CUDA

Solutions: