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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are tasked with comparing the performance of different GPU-accelerated frameworks for a deep learning model. The frameworks you are considering are TensorFlow, PyTorch, and CUDA. To evaluate the performance, you decide to implement a benchmark that measures GPU efficiency, memory usage, and speed.
Which of the following actions should you take to design an effective benchmark? (Select two)

A) Use a batch size that is optimal for each framework's memory management.
B) Use CPU-based implementations of the same frameworks for comparison.
C) Benchmark only the training phase of the deep learning model.
D) Measure GPU utilization and memory usage, but ignore the network and disk I/O.
E) Use a common dataset for all frameworks to ensure comparability.

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

3. You are tasked with acquiring a dataset for training a machine learning model in healthcare, predicting patient readmission rates. Before using the dataset, you must assess its quality.
Which of the following is the most important factor to evaluate before acquisition?

A) The programming language used to preprocess the dataset
B) The file format (CSV, JSON, or XML) of the dataset
C) Whether the dataset is stored on a cloud server or a local machine
D) The number of missing values and inconsistencies in key columns

4. 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()

5. You are working on a data science project where you need to process a large dataset containing
500 million records. You want to determine whether GPU acceleration would significantly improve performance.
Which of the following factors best indicates that you should use an accelerated computing solution like RAPIDS?

A) The dataset is heavily structured but mainly requires text-based analysis using regex-based search and manipulation.
B) The dataset has high-dimensional sparse features and requires complex operations such as nearest neighbor search and clustering.
C) The dataset consists of simple arithmetic operations on a few columns and can be processed using vectorized NumPy operations.
D) The dataset is a structured table with less than 100,000 records and can be handled efficiently with a Pandas DataFrame.

Solutions: