Try and practice the latest NVIDIA : NCP-ADS real questions & answers

NVIDIA NCP-ADS Exam Overview:

NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a predictive maintenance model for industrial equipment. The dataset includes various sensor readings, categorical metadata, and timestamped events.
Which data type is the best choice for a feature representing the operating status of a machine, which has three possible states: "Idle", "Running", and "Error"?

A) Text (storing states as raw strings like "Idle", "Running", "Error").
B) Categorical encoding (one-hot encoding or ordinal encoding).
C) Floating-point representation (e.g., 0.1 for Idle, 0.5 for Running, 0.9 for Error).
D) Integer (0, 1, 2) to represent each state numerically.

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

3. You are working on a medium-sized dataset (~500,000 rows, 20 columns) and need to perform fast exploratory data analysis (EDA) with filtering, aggregations, and transformations.
Which of the following Python libraries would be the most efficient choice for this task?

A) Pandas
B) Vaex
C) Dask
D) PySpark

4. You are conducting rapid experimentation on an NVIDIA GPU to determine the best trade-off between model accuracy and inference latency.
Which approach is the most efficient for systematically evaluating multiple configurations?

A) Test different model configurations on a CPU first before moving to the GPU for final evaluation
B) Train each possible model variation from scratch to evaluate accuracy and performance differences
C) Reduce training epochs significantly to save time, even if the model is underfitting
D) Use automated hyperparameter tuning tools like Optuna or Ray Tune with mixed precision training

5. A data engineering team is tasked with processing terabytes of log data every hour using an ETL pipeline. Due to the large data volume, they need a scalable GPU-accelerated solution that can distribute data processing across multiple GPUs.
Which approach best meets their needs?

A) Process data using Pandas, then export the results to a CSV file for GPU-accelerated analytics.
B) Use NumPy for data transformations before converting the dataset into cuDF for final storage.
C) Use cuDF alone for processing log data, as it provides optimal performance on a single GPU.
D) Use Dask-cuDF to distribute cuDF DataFrame operations across multiple GPUs, enabling parallel ETL processing.

Solutions: