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NVIDIA-Certified-Professional Accelerated Data Science 認定 NCP-ADS 試験問題:

1. A company is deploying an MLOps pipeline for training and serving deep learning models. The data scientists want to leverage GPU acceleration at multiple stages of the pipeline to enhance efficiency.
Which of the following steps would benefit the most from GPU acceleration?

A) Model monitoring by logging metadata and performance metrics in a database.
B) Storing and retrieving models from a centralized object storage system.
C) Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.
D) Training and inference workloads using deep learning models with TensorFlow or PyTorch.

2. You are processing a large dataset using RAPIDS cuDF and Dask-cuDF on an NVIDIA GPU. Your profiling indicates that data transfer times between CPU and GPU are significantly slowing down your pipeline.
What is the most effective way to reduce this bottleneck?

A) Convert the dataset into a CSV format before transferring it to the GPU
B) Increase the CPU RAM allocation to store more data before transferring to the GPU
C) Transfer data in multiple smaller chunks to the GPU instead of larger batches
D) Use cudf.read_parquet() instead of Pandas to load data directly into GPU memory

3. A data scientist is preparing a dataset containing numerical features with varying scales and distributions. Some features range from 0 to 1, while others have values ranging from -5000 to 5000. The dataset will be used in a machine learning model that relies on gradient-based optimization.
What is the best approach to standardizing the data to ensure uniformity across features?

A) Keep the features as they are since machine learning models can handle varying scales naturally
B) Use min-max scaling to transform all features into the range [0,1]
C) Normalize the dataset by dividing each feature by its maximum absolute value
D) Apply z-score normalization (standardization) to scale features to have a mean of 0 and a standard deviation of 1

4. You are working with large datasets in cuDF and have noticed significant performance bottlenecks due to repeated computation and excessive shuffling in your workflow. You want to use data caching to optimize the execution plan and reduce redundant operations.
Which of the following is the best way to implement data caching in cuDF to avoid repeated recomputation and excessive shuffling?

A) Store intermediate DataFrames as temporary CSV files on disk and reload them when needed to simulate a cache.
B) Use the .to_pandas() method to convert the cuDF DataFrame into a Pandas DataFrame, leveraging CPU caching instead of GPU caching.
C) Use the .reset_index() method after performing transformations to force materialization of the dataset and cache results.
D) Use the .persist() method on a cuDF DataFrame to store intermediate results in GPU memory, reducing redundant calculations.

5. You are tasked with optimizing an ETL pipeline that processes petabytes of data daily. Your organization is already using Apache Spark for distributed data processing but is experiencing performance bottlenecks. You need a solution that improves execution speed without requiring extensive code modifications.
Which of the following solutions best meets your needs?

A) Rewriting the entire ETL pipeline in CUDA to maximize GPU efficiency
B) Switching to Dask and RAPIDS to fully utilize GPU acceleration
C) Using the NVIDIA Spark RAPIDS Accelerator to run Spark workloads on GPUs
D) Using Apache Arrow to optimize in-memory columnar processing within Spark

質問と回答：