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

1. You are designing an ETL pipeline to process terabytes of financial transaction data in real time.
The pipeline consists of:
Extracting data from multiple sources (CSV, Parquet, and SQL databases), Transforming the data using operations such as filtering, joins, and aggregations, Loading the processed data into a data lake for analytics.
Given that you are using NVIDIA RAPIDS cuDF for GPU-accelerated ETL, which of the following approaches optimizes performance while ensuring scalability?

A) Load all data into a single, large cuDF DataFrame before performing transformations
B) Convert cuDF DataFrames to Pandas DataFrames before performing transformations for compatibility
C) Use CPU-based ETL frameworks such as Apache Spark without GPU acceleration
D) Use cuDF to read and process the data in batches, leveraging Dask-cuDF for distributed computation when necessary

2. You need to determine the optimal data processing library for a small dataset of 500,000 records that will be processed on a multi-core CPU machine with no GPU access.
Which of the following libraries would be the most efficient for this task?

A) pandas
B) Dask
C) cuDF
D) PyTorch

3. You are building a predictive model for retail sales forecasting and need a dataset that includes historical sales transactions, customer demographics, and external economic indicators (e.g., inflation rate, unemployment rate).
Which of the following datasets would be the most appropriate for your model?

A) A public dataset of annual GDP per country
B) A dataset containing transaction history and customer profiles from a retail company
C) A dataset of product reviews and customer sentiments from an e-commerce website
D) A dataset with global temperature trends over the past decade

4. You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?

A) NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.
B) NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.
C) NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.
D) NVIDIA Nsight Systems for managing dataset storage and retrieval performance.

5. You have trained a machine learning model and are deploying it into production in a cloud-based environment. You need to ensure the model is continuously monitored for performance, including drift in input data and prediction quality.
Which of the following techniques should you use to effectively monitor the model in production? (Select two)

A) Implement model versioning and rollback procedures to maintain consistency in production models.
B) Set up continuous model retraining pipelines triggered by data drift or performance degradation.
C) Use manual inspection of model predictions periodically to assess accuracy in production.
D) Restrict access to the model's API to minimize the number of calls and reduce monitoring overhead.

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