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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?

A) TensorRT
B) NVIDIA A100
C) NVIDIA Tesla
D) NVIDIA Jetson

2. You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

A) Ensuring the ETL pipeline uses only batch processing for data ingestion.
B) Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
C) Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.
D) Using a single storage location for both raw and transformed data to simplify the workflow.
E) Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.

3. You are working with cuGraph to analyze a large social network dataset where users are represented as nodes, and their connections (friendships) are represented as edges. You need to determine the most efficient way to store and process the graph in cuGraph for high-performance analytics.
Which of the following graph representations is best suited for efficient processing in cuGraph?

A) Compressed Sparse Row (CSR) format
B) Adjacency List representation using Python dictionaries
C) Incidence Matrix representation
D) Edge List stored in a Pandas DataFrame

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

5. You are analyzing a dataset containing millions of transaction records to detect fraudulent transactions. The dataset is highly imbalanced, with fraudulent cases making up only 0.5% of the data.
Which data analysis strategy will most effectively handle the imbalance while minimizing the risk of overfitting?

A) SMOTE (Synthetic Minority Over-sampling Technique)
B) Random undersampling of the majority class
C) Stratified random sampling
D) Random oversampling of the minority class

Solutions: