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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. You are considering using a multi-GPU setup to accelerate training a large deep learning model.
Which of the following are important factors to consider when deciding whether to use single-GPU or multi-GPU training? (Select two)

A) The performance of multi-GPU training scales linearly with the number of GPUs, meaning adding more GPUs will always result in a proportional reduction in training time.
B) The increase in training time from using a multi-GPU setup is negligible, as the overhead from communication and synchronization is minimal.
C) Multi-GPU setups require proper load balancing and efficient gradient synchronization, as uneven distribution of work can lead to suboptimal performance.
D) The success of multi-GPU training depends heavily on the ability to increase the batch size without exceeding memory limits.

3. You are processing a dataset with billions of records and want to encode a categorical column efficiently using NVIDIA RAPIDS.
Which of the following methods correctly encodes categorical data using cuDF?

A) df['category_column'] = LabelEncoder().fit_transform(df['category_column'])
B) df['category_column'] = df['category_column'].astype('category')
C) df['category_column'] = df['category_column'].apply(lambda x: hash(x) % 1000)
D) df['category_column'] = df['category_column'].one_hot_encode()

4. A data scientist is setting up a RAPIDS AI environment for a machine learning project that requires CUDA-enabled libraries and specific package versions to avoid conflicts.
Which of the following approaches best ensures a stable and reproducible environment while leveraging NVIDIA technologies?

A) Use Conda with the rapidsai channel to create an isolated environment that includes cuDF, cuML, and other GPU-accelerated libraries.
B) Use a system-wide Python installation and install packages globally to ensure consistency across different users.
C) Manually install each required package with pip to ensure the latest versions are used without interference from Conda.
D) Install CUDA and RAPIDS AI libraries using OS-level package managers such as apt or yum instead of Conda or Docker.

5. Which of the following best describes the functionality of DLProf in deep learning model profiling?

A) DLProf can only profile the CPU side of a deep learning model's performance.
B) DLProf is used for visualizing model predictions, similar to TensorBoard.
C) DLProf tracks GPU utilization, memory bandwidth, and kernel execution time to identify performance bottlenecks.
D) DLProf uses statistical methods to predict future model performance based on historical data.

Solutions: