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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

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

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

Question 2

You are working on a large-scale social network analysis project using cuGraph. Your goal is to identify the most influential individuals in the network based on their connectivity and reach.
Which of the following cuGraph algorithms would be the most appropriate choice for this task?

A. Connected Components
B. Single Source Shortest Path (SSSP)
C. Louvain Clustering
D. PageRank

Question 3

You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?

A. Use fillna() with a fixed value on the GPU using cuDF
B. Apply a deep learning-based imputation model before moving data to the GPU
C. Convert the dataset to a NumPy array and manually replace missing values with the mean
D. Drop all rows containing missing values using Pandas before transferring data to the GPU

Question 4

Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A. Z-score Standardization
B. Robust Scaling
C. Min-Max Scaling
D. Log Transformation

Question 5

You have deployed a deep learning model for image classification in a production environment, but inference latency is high. You need to optimize the model to reduce response time while maintaining accuracy.
Which NVIDIA technology is best suited for this task?

A. NVIDIA TensorRT to optimize and accelerate deep learning inference by reducing model size and execution time.
B. NVIDIA Clara Imaging to improve deep learning inference for image classification workloads.
C. NVIDIA RAPIDS cuML to optimize deep learning inference using GPU-accelerated ML algorithms.
D. NVIDIA DeepStream to process image classification models for low-latency inference in batch mode.

Solutions: