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

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data engineering team is designing an ETL pipeline to process large-scale financial transaction data. They want to leverage NVIDIA-accelerated ETL tools to extract data from a data lake, transform it by filtering and aggregating key fields, and load it into a data warehouse.
Which of the following approaches provides the most efficient ETL processing using NVIDIA technologies?

A) Perform all transformations using Pandas DataFrames before loading the data into the GPU
B) Write a custom ETL script in pure Python to handle data extraction, transformation, and loading
C) Use RAPIDS cuDF to preprocess data in-memory and BlazingSQL to accelerate SQL-based transformations
D) Use Dask on CPUs for distributed ETL processing and later move results to a GPU-based database

2. A data science team wants to deploy a GPU-accelerated pipeline using cuGraph to analyze graph data on cloud infrastructure. They are evaluating different cloud-based GPU solutions.
Which of the following factors should they consider when selecting a cloud-based GPU instance for running cuGraph efficiently?

A) Cloud-based GPUs are only useful for rendering graphics, not for running cuGraph algorithms.
B) cuGraph runs equally well on CPU-based virtual machines, making GPU instances unnecessary.
C) The choice of GPU instance does not affect cuGraph performance since all GPUs execute graph algorithms at the same speed.
D) The availability of NVIDIA CUDA-enabled GPUs, as cuGraph requires CUDA for acceleration.

3. You are working with a dataset containing billions of rows and need to perform data transformations, aggregations, and joins efficiently on a single-node GPU-enabled workstation.
Which NVIDIA technology is best suited to optimize performance for these operations?

A) NVIDIA TensorRT to optimize DataFrame transformations and aggregations using deep learning.
B) NVIDIA RAPIDS cuDF to leverage GPU acceleration for large-scale DataFrame operations.
C) NVIDIA Triton Inference Server to accelerate data processing workflows on a single GPU.
D) NVIDIA Nsight Compute to profile and optimize the performance of GPU-based aggregations.

4. You are building a fraud detection system and have a dataset that includes a feature representing transaction amounts. The values range from a few cents to several thousand dollars.
What is the most appropriate data type for this feature?

A) Categorical (grouping transactions into bins such as "low", "medium", "high").
B) Integer (rounding all transaction amounts to whole dollars).
C) Boolean (True for transactions above $100, False otherwise).
D) Floating-point (with precision to store cents or fractions of a cent).

5. You are tasked with cleansing a dataset containing numerical data that has significant outliers.
You're using pandas to identify and appropriately handle these outliers before applying CuDF for accelerated downstream analysis.
Which method effectively manages the numerical outliers while preserving the dataset's integrity for subsequent accelerated analytics?

A) Remove outliers completely using pandas.DataFrame.drop().
B) Replace outliers with zero using pandas.Series.replace().
C) Clip outliers using pandas.Series.clip() based on percentile thresholds (e.g., 5th and 95th percentile).
D) Fill outlier values with mean using pandas.Series.fillna().

Solutions: