NVIDIA-Certified-Professional Accelerated Data Science : NCP-ADS

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NVIDIA NCP-ADS 考試大綱主題:

最新的 NVIDIA-Certified Professional NCP-ADS 免費考試真題：

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

2. You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?

A) Convert both DataFrames to Pandas before performing the join for compatibility
B) Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining
C) Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF
D) Perform the join operation entirely in a CPU-based Spark environment for better stability

3. Your data science team is performing exploratory data analysis (EDA) on a large GPU-accelerated environment using cuDF and Dask-cuDF. During analysis, queries on categorical columns are performing poorly.
Which approach will most effectively improve query performance for categorical data in GPU-accelerated DataFrames?

A) Increase GPU memory allocation
B) Use CPU-based DataFrames (pandas) instead of GPU-based DataFrames
C) Convert categorical columns to integer codes
D) Store data in compressed file formats like Parquet

4. You are training a convolutional neural network (CNN) model with a large dataset on a single GPU.
The model is consuming too much GPU memory, and training is slow.
Which of the following techniques would help you reduce GPU memory consumption while maintaining or improving the efficiency of training? (Select two)

A) Use a smaller batch size to reduce the memory requirements per training iteration, allowing for faster training.
B) Decrease the size of the model by reducing the number of layers or the number of filters per layer.
C) Use batch normalization to reduce memory usage by skipping some of the computations during training.
D) Use automatic differentiation for every operation to save memory during the backpropagation phase.
E) Implement mixed precision training to lower memory usage and speed up computation without losing accuracy.

5. You are working on a large-scale machine learning project that requires preprocessing terabytes of structured and semi-structured data. You need a distributed data processing framework that can leverage NVIDIA GPUs efficiently to accelerate computations.
Which of the following approaches would best achieve this goal?

A) Using plain NumPy with CUDA extensions to manually parallelize computations across multiple GPUs
B) Using Apache Spark with PySpark for CPU-based distributed data processing
C) Using TensorFlow's Dataset API to load and preprocess the data on GPUs
D) Using Dask with RAPIDS cuDF and cuML for distributed GPU-accelerated processing

問題與答案：