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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on an MLOps project where GPU-accelerated workflows are being used for model training. You want to benchmark and optimize these workflows to ensure the best performance.
Which of the following steps should you consider to effectively benchmark and optimize GPU- accelerated workflows? (Select two)

A) Increase the batch size and learning rate simultaneously to maximize GPU usage and reduce training time.
B) Use a dynamic batch size strategy that adjusts the batch size based on available GPU memory to maximize throughput.
C) Use profiling tools to measure the GPU utilization and memory usage during training to identify performance bottlenecks.
D) Optimize data loading by using data augmentation techniques during training to reduce the time spent on I/O operations.

2. You are preprocessing a dataset using NVIDIA RAPIDS cuDF and need to handle missing values in the column temperature by replacing them with the column's median value.
Which of the following approaches correctly achieves this in an optimized manner?

A) df['temperature'].fillna(df['temperature'].mean(), inplace=True)
B) df['temperature'].dropna(inplace=True)
C) df['temperature'].fillna(df['temperature'].median(), inplace=True)
D) 1. df['temperature'] = df['temperature'].map(2. lambda x: df['temperature'].median() if x is None else x
3.)

3. You are working with a cuDF DataFrame and need to convert a column named sales from float64 to int32 to save memory.
Which of the following is the correct and most efficient way to perform this conversion in cuDF?

A) df['sales'] = df['sales'].to_numeric('int32')
B) df['sales'].apply(lambda x: int(x))
C) df['sales'] = df['sales'].astype('int32')
D) df['sales'].convert_dtypes('int32')

4. A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.
The dataset contains:
A low-cardinality categorical feature (Product Type) with 10 unique values.
A high-cardinality categorical feature (User ID) with 100,000 unique values.
A numerical feature (Price) that requires transformation.
Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

A) Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.
B) Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.
C) Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.
D) Frequency encoding for User ID is an efficient alternative to one-hot encoding, as it replaces each category with its frequency in the dataset, reducing dimensionality while preserving useful information.
E) Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision.
F) Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.

5. A research team is analyzing large-scale social interactions and wants to identify strongly connected communities within a massive graph dataset using NVIDIA's cuGraph library.
Which method would be the most efficient for this task?

A) Run the cuGraph PageRank algorithm and classify nodes with high scores as community leaders.
B) Apply cuGraph's Dijkstra's algorithm to find the shortest paths between all nodes and group them into communities.
C) Apply cuGraph's Label Propagation Algorithm (LPA) to divide the graph into communities without predefining the number of clusters.
D) Use cuGraph's Louvain method to detect hierarchical communities based on modularity optimization.

Solutions: