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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?

A) Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution
B) Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
C) Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs
D) Use Dask DataFrame to parallelize deep learning model training across multiple GPUs

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

3. In Python, when working with large datasets using pandas, which of the following methods are best for improving performance and efficiency when applying operations on DataFrames? (Select two)

A) Using for loops to apply operations row by row
B) Using map() function to apply a function element-wise
C) Using apply() function over DataFrame rows
D) Using iterrows() for iterating through DataFrame rows
E) Using vectorized operations (e.g., element-wise arithmetic)

4. You are a data scientist analyzing a social media network with NVIDIA cuGraph to identify the most influential users using the PageRank algorithm.
Which option best describes how cuGraph PageRank operates on a directed graph?

A) PageRank in cuGraph uses an iterative power method to update node importance values based on incoming edges, incorporating a damping factor to handle random jumps.
B) PageRank in cuGraph operates only on undirected graphs and cannot be applied to networks where edges have a direction.
C) PageRank assigns equal importance to all nodes in the graph initially and updates values only based on outgoing edges, ignoring incoming edges.
D) PageRank in cuGraph is a label propagation algorithm that clusters nodes into communities rather than ranking their importance.

5. You are a data scientist working on a large-scale deep learning project that requires significant computational resources. You have the option to run your workloads on a cloud-based GPU instance.
Which of the following statements best describes a key benefit of using cloud-based GPUs for your workload?

A) Cloud-based GPUs enable scalable resource allocation, allowing you to dynamically increase or decrease GPU instances as needed.
B) Cloud-based GPUs provide consistent and predictable performance, identical to on-premise dedicated GPUs.
C) Cloud-based GPUs eliminate all data transfer bottlenecks and latencies when training models on large datasets.
D) Cloud-based GPUs are always more cost-effective than on-premise GPUs, regardless of workload size and duration.

Solutions: