Stay organized with collections Save and categorize content based on your preferences. Speed up data analytics with GPUs quiz Return to pathway What open-source library does cuDF accelerate? (Select all that apply) Choose as many answers as you see fit. pandas Polars Apache Spark Oracle Before importing cuDF in a Colab Enterprise notebook, what configuration step must you take regarding the Runtime? Increase the disk size to 500GB or more Select a Runtime specifically equipped with a GPU (e.g. L4) Enable "High-RAM" mode on a standard CPU runtime Mount Google Drive to store the temporary CUDA kernels While attempting to run cuDF in Colab Enterprise, you receive an error indicating "no CUDA-capable device is detected," what is the most likely cause? The dataset is too small to require a GPU Google Cloud billing is currently disabled You have not installed standard pandas The notebook runtime does not have a GPU GPU-accelerating pandas requires zero code changes when using NVIDIA cuDF True False When using Colab Enterprise with NVIDIA cuDF, where does df.read_csv('/content/data.csv') primarily load the data to? Google Cloud Storage buckets The virtual machine's CPU RAM The attached GPU's VRAM Your local laptop's hard drive Which of the following are valid methods to activate cuDF pandas acceleration? (Select all that apply) Choose as many answers as you see fit. Using the magic command %load_ext cudf.pandas in a notebook Renaming your .py file to .cu_py Adding import cudf.pandas; cudf.pandas.install() prior to importing pandas Running a script via python -m cudf.pandas myscript.py When using cudf.pandas, what happens if a specific pandas function is not yet GPU-accelerated? Graceful fall back to CPU execution NotImplementedError is raised and execution is halted Function is ignored and execution continues Submit answers error_outline An error occurred when grading the quiz. Please try again.
