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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. You are training a machine learning model using NVIDIA RAPIDS cuML and notice that the training process is significantly slower than expected. You suspect that there are bottlenecks in data movement and computation.
Which of the following techniques can best help you diagnose and resolve these bottlenecks?

A) Use cudf.DataFrame.to_pandas() to convert the dataset to a pandas DataFrame for analysis.
B) Move all data from GPU memory to CPU memory before training the model.
C) Use cuml.common.device_auto_mem_size() to check GPU memory usage and adjust batch sizes accordingly.
D) Reduce the number of features used in training without profiling the actual bottlenecks.

3. You are working with a dataset containing hundreds of millions of records, and you need to perform ETL operations such as filtering, joins, and aggregations. Given the dataset size, which NVIDIA- accelerated library should you use to achieve optimal performance?

A) Pandas, as it is widely used and supports all common DataFrame operations, even for very large datasets.
B) cuDF, as it provides GPU-accelerated DataFrame operations similar to Pandas, allowing for efficient processing of large datasets.
C) cuPy, because it provides GPU-accelerated array operations, making it the best option for processing tabular data.
D) NumPy, because it is optimized for numerical computing and offers better performance for handling tabular data.

4. You are comparing the performance of GPU-accelerated deep learning models on two cloud platforms: AWS EC2 and Google Cloud Platform (GCP). You want to design a benchmark that evaluates GPU resource utilization, processing time, and cost-efficiency for training models with large datasets.
Which actions should you take to implement an effective benchmark? (Select two)

A) Limit the benchmarking tests to a single model architecture for each cloud platform.
B) Measure both GPU utilization and network latency between the cloud platform and your data storage location.
C) Only consider the cost of GPU resources for each cloud provider without factoring in data storage and transfer costs.
D) Run each model on the same GPU type and instance size across both cloud platforms.

5. A research team is analyzing a large transportation network and wants to identify groups of locations that are closely connected based on travel routes.
Which NVIDIA cuGraph function would be the most effective for this task?

A) Use cuGraph's shortest_path() to compute all shortest paths and derive clusters manually.
B) Use cuGraph's pagerank() to rank the most connected locations.
C) Apply cuGraph's bfs() (Breadth-First Search) to find clusters of locations.
D) Use cuGraph's louvain() function to detect communities within the graph.

Solutions: