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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are analyzing a dataset that contains missing values.
Which of the following techniques is most appropriate when dealing with missing numerical data in a dataset, ensuring minimal impact on model performance?

A) Replacing missing values with a constant value (e.g., zero)
B) Replacing missing values with the mean of the column
C) Using k-nearest neighbors (KNN) imputation
D) Removing rows with missing values

2. You are working on a large-scale graph analysis problem that involves computing the shortest paths between nodes in a massive social network dataset. You decide to leverage NVIDIA RAPIDS cuGraph for accelerated computation.
Which of the following cuGraph functions should you use?

A) cugraph.pagerank()
B) cugraph.sssp()
C) cugraph.k_truss()
D) cugraph.label_propagation()

3. You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?

A) Pandas with multiprocessing
B) RAPIDS + Dask for distributed GPU-accelerated ETL
C) Apache Spark with its default CPU-based execution
D) Hadoop MapReduce

4. A machine learning engineer is training a convolutional neural network (CNN) on an NVIDIA GPU and needs to maximize throughput while avoiding OOM errors.
Which of the following techniques is the most effective way to balance memory efficiency and training speed?

A) Loading all dataset samples into GPU memory at the start of training
B) Using dynamic batch sizing based on available GPU memory
C) Using a batch size of 1 to minimize memory usage
D) Allocating a fixed batch size without monitoring memory usage

5. You are designing a reproducible benchmark to compare the performance of deep learning models across frameworks like PyTorch and TensorFlow using NVIDIA's A100 GPU.
Which step is most critical in ensuring fair benchmarking conditions?

A) Enabling XLA compiler optimizations only for TensorFlow to enhance its performance.
B) Using a different precision setting for each framework to maximize performance per framework's capabilities.
C) Ensuring the same CUDA/cuDNN and driver versions are installed when running benchmarks across frameworks.
D) Measuring only forward pass latency to compare inference speed while ignoring backward pass computation.

Solutions: