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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are processing a large-scale transportation network graph using NVIDIA cuGraph. The graph is extremely large, consuming almost all available GPU memory. Performance is deteriorating, and some computations fail due to memory exhaustion.
What is the best approach to efficiently handle this large graph while keeping computations on the GPU?

A) Convert the graph into a NetworkX graph and process it on the CPU to reduce GPU memory usage.
B) Use cuGraph's multi-GPU support via Dask-cuGraph to distribute the graph across multiple GPUs.
C) Store the graph as a large Python dictionary and use cuGraph only for specific queries.
D) Manually split the graph into chunks and process each chunk separately without any coordination.

2. You are working with a large dataset using NVIDIA RAPIDS cuDF and need to normalize a numerical column (price) to scale its values between 0 and 1.
Which of the following approaches correctly normalizes the column using cuDF?

A) df["price"] = df["price"].applymap( 2. lambda x: (x - df["price"].min()) 3. / (df["price"].max() - df["price"].min()) 4. )
B) df["price"] = ( 2. df["price"] - df["price"].min() 3. ) / (df["price"].max() - df["price"].min())
C) df["price"] = (df["price"] - df["price"].mean()) / df["price"].std()
D) df["price"] = df["price"] / df["price"].max()

3. Which of the following scenarios are most appropriate for using GPU acceleration when working with large-scale datasets in machine learning? (Select two)

A) Performing exploratory data analysis (EDA) on a dataset of 100,000 rows with 10 features.
B) Running a deep learning model for image classification with millions of labeled images.
C) Running a large ensemble of simple models (e.g., random forests) on a dataset of 10 million rows.
D) Training a large-scale natural language processing (NLP) model on text data with billions of words.
E) Using decision trees for a classification task with a dataset of 1 million rows and 20 features.

4. You are analyzing a transportation network where airports represent nodes and flight routes represent edges. You need to determine the most critical airports in the network based on how many shortest paths pass through them.
Which cuGraph centrality algorithm should you use for this task?

A) Closeness Centrality, as it measures how close an airport is to all other airports, making it the best indicator of global connectivity.
B) Triangle Count, as it determines the number of three-node cycles an airport is part of, indicating its importance in network structure.
C) Degree Centrality, as it measures the number of direct connections an airport has, indicating its importance in the network.
D) Betweenness Centrality, as it identifies airports that act as major transit hubs by measuring how frequently they appear in shortest paths.

5. You are working with a time-series dataset containing network traffic logs. You suspect the presence of anomalies, such as distributed denial-of-service (DDoS) attacks or sudden traffic surges.
Which machine learning approach is best suited for detecting these anomalies?

A) Linear Regression
B) k-Nearest Neighbors (k-NN) for Classification
C) Naive Bayes Classifier
D) Autoencoder-based Anomaly Detection

Solutions: