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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

You are working on a large dataset for a machine learning model and need to preprocess the data efficiently using NVIDIA RAPIDS cuDF on a GPU-accelerated system.
Which of the following statements is correct regarding data preparation using cuDF?

A. cuDF supports common Pandas-like operations, such as filtering, merging, and groupby, but performs them efficiently on the GPU.
B. cuDF requires all input data to be stored in Apache Parquet format, as it does not support CSV or JSON ingestion.
C. cuDF operations are executed lazily and require an explicit compute() call to execute transformations.
D. cuDF can be used as a direct replacement for Pandas, but it is optimized for multi-node distributed computing rather than GPU acceleration.

Question 2

What is the primary advantage of using NVIDIA Triton Inference Server for deploying and monitoring machine learning models in production?

A. It provides GPU optimization to handle high-throughput inference workloads.
B. It only supports TensorFlow models for inference.
C. It automatically tunes hyperparameters for all models.
D. It is designed solely for edge devices and not for data centers.

Question 3

You are optimizing a deep learning model that runs on an NVIDIA GPU and notice that inference latency is unexpectedly high. You decide to use DLProf to analyze the model's execution profile. After running the profiler, you find that a significant portion of execution time is spent on a single GPU kernel.
Which of the following actions would best help you identify and optimize this performance bottleneck?

A. Modify the neural network architecture to use more convolutional layers, as this generally improves execution speed on NVIDIA GPUs.
B. Use DLProf's Tensor Core Analysis feature to determine if Tensor Cores are being utilized effectively.
C. Switch to a CPU-based execution environment, as it will eliminate any potential GPU bottlenecks.
D. Reduce the batch size to minimize the time spent on memory-bound operations and improve kernel efficiency.

Question 4

A data scientist is training a deep learning model on an NVIDIA GPU but notices that the training speed is not significantly faster than when using a CPU.
Which of the following strategies is the best approach to fully utilize GPU acceleration and optimize training performance?

A. Using mixed precision training with NVIDIA Tensor Cores
B. Disabling cuDNN optimizations to allow more flexibility in kernel execution
C. Increasing the batch size beyond the GPU's memory limit
D. Using only CPU-based data augmentation to keep the GPU dedicated to training

Question 5

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

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

Solutions: