NVIDIA NVIDIA-Certified Professional NCP-ADS

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NVIDIA NCP-ADS 試験シラバストピック:

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

1. You are working with a large dataset containing customer transactions and want to perform exploratory data analysis (EDA) efficiently. Given the dataset's size, you decide to use NVIDIA RAPIDS to accelerate the process.
Which of the following approaches is the most effective for conducting EDA using NVIDIA technologies?

A) Use TensorFlow and Keras to preprocess the data before performing EDA
B) Use RAPIDS cuDF to perform fast dataframe operations and visualize results with cuXfilter
C) Perform SQL queries on a CPU-based database for initial data analysis before GPU acceleration
D) Load the dataset into Pandas and use Matplotlib for visualization

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

3. You are designing a data pipeline for a healthcare analytics company that processes petabytes of structured and semi-structured patient data. Your goal is to optimize performance while maintaining accuracy.
Which of the following strategies is the most appropriate choice for accelerating this workload?

A) Use RAPIDS cuDF for data ingestion, preprocessing, and transformation, then offload model training to scikit-learn on CPUs.
B) Use RAPIDS cuDF and Dask-cuDF for distributed preprocessing and RAPIDS cuML for GPU- accelerated machine learning model training.
C) Use Apache Hadoop and MapReduce for preprocessing before training models on a single GPU.
D) Load the data into a PostgreSQL database and run SQL queries for all transformations before training a model.

4. A financial analyst is working with an irregularly spaced time-series dataset containing cryptocurrency transactions. The timestamps are not evenly distributed, with some periods having dense data and others having sparse entries. The analyst wants to visualize the data efficiently using GPU acceleration.
What is the best preprocessing approach before visualization?

A) Convert the dataset to Pandas and use df.resample() to aggregate by fixed time intervals.
B) Resample the time-series to a fixed frequency using cuDF.resample() and fill missing values.
C) Sort the data by timestamp and drop all sparse regions using df.dropna().
D) Ignore the irregularity and plot the raw timestamps directly without any preprocessing.

5. A financial institution is developing an ETL pipeline to ingest and process large volumes of streaming data from various sources, including stock market feeds, real-time transactions, and economic indicators. The ETL process must be highly efficient to minimize latency while ensuring data integrity.
Which of the following strategies is best suited for implementing a high-performance, GPU-accelerated ETL pipeline?

A) Utilize NVIDIA Morpheus with RAPIDS to preprocess real-time streaming data using GPU acceleration.
B) Load data directly into an Excel spreadsheet and use VBA macros to clean and transform it.
C) Use Pandas and Python's built-in threading library to handle concurrent data ingestion and transformation.
D) Store all streaming data in a PostgreSQL database before performing batch transformations.

質問と回答：