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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are preprocessing a dataset using NVIDIA RAPIDS cuDF and need to handle missing values in the column temperature by replacing them with the column's median value.
Which of the following approaches correctly achieves this in an optimized manner?

A) 1. df['temperature'] = df['temperature'].map(2. lambda x: df['temperature'].median() if x is None else x
3.)
B) df['temperature'].fillna(df['temperature'].mean(), inplace=True)
C) df['temperature'].fillna(df['temperature'].median(), inplace=True)
D) df['temperature'].dropna(inplace=True)

2. 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) TXT
B) JSON
C) CSV
D) Parquet

3. You are implementing a Dask-based solution for distributed data parallelism across a multi-GPU system.
Which configuration steps would ensure effective use of GPUs for parallel computation? (Select two)

A) Use dask_cudf to convert DataFrame computations into GPU-accelerated operations using cuDF
B) Use Dask's Cluster class with the distributed scheduler and specify CPU cores only for GPU workloads
C) Use dask_cuda's LocalCUDACluster with proper GPU memory management to handle multiple GPUs
D) Use dask_cuda's LocalCUDACluster and let Dask automatically allocate GPUs without any configuration
E) Create a LocalCUDACluster and manually specify the GPUs you want to use for each Dask worker

4. A company is processing large log files from a cloud application, accumulating over 5TB of data daily. The data processing pipeline must be GPU-accelerated to extract insights quickly.
Which of the following is the most effective approach to handle high-volume log processing using NVIDIA technologies?

A) Leverage Dask-cuDF to distribute the dataset across multiple GPUs, ensuring efficient parallel processing.
B) Store logs as Pandas DataFrames and use multiprocessing to parallelize operations across CPU cores.
C) Use RAPIDS cuML for performing log file processing, taking advantage of its optimized ML algorithms.
D) Use cuDF with explicit memory management to load and process the entire dataset into a single GPU.

5. A machine learning engineer is working on a multi-GPU workload using Dask and RAPIDS to process a massive dataset efficiently. However, they notice that GPU utilization is not optimal, and data transfer between GPUs is slowing down computation.
What is the best approach to minimize data transfer overhead and maximize parallel efficiency?

A) Load the dataset into a Pandas DataFrame and distribute computations across GPUs manually
B) Use NVLink to enable high-bandwidth inter-GPU communication and dask_cuda.LocalCUDACluster
C) Use Dask's single-threaded scheduler to avoid multi-threading overhead
D) Transfer data between GPUs using NumPy's .copy() function

Solutions: