NVIDIA NCP-ADS : NVIDIA-Certified-Professional Accelerated Data Science

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are training a large-scale random forest model on a dataset with millions of rows and hundreds of features. The training time is significantly high when using traditional CPU-based machine learning frameworks.
Which NVIDIA technology should you use to accelerate training while maintaining compatibility with common ML frameworks like scikit-learn?

A) NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.
B) NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.
C) NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.
D) NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.

2. You are working on an MLOps pipeline with a large dataset using NVIDIA technologies. You need to assess the memory size of the dataset before training the model to avoid memory overflow errors.
Which of the following is the best method for verifying the memory size of the dataset on the GPU using NVIDIA tools?

A) Load the dataset into a CPU-based pandas dataframe and use the pandas.memory_usage() function to estimate the dataset's size before transferring it to the GPU.
B) Use cuDF to load the dataset into GPU memory and use the nvidia-smi tool to monitor GPU memory usage during dataset loading.
C) Use PyTorch's torch.cuda.memory_allocated() function to check the memory allocated on the GPU after the dataset is loaded.
D) Use NVIDIA DALI to process the dataset and directly query the memory size through DALI's DataLoader function.

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

4. You are working on a machine learning pipeline using NVIDIA RAPIDS cuML and need to standardize the dataset to ensure that all features have a mean of 0 and a standard deviation of 1.
Which of the following methods should you use to achieve this in cuML?

A) cuml.preprocessing.LabelEncoder()
B) cuml.preprocessing.MinMaxScaler()
C) cuml.preprocessing.OneHotEncoder()
D) cuml.preprocessing.StandardScaler()

5. You are selecting a dataset for GPU-accelerated data science using NVIDIA RAPIDS.
Which dataset characteristic is most suitable for taking advantage of GPU acceleration?

A) A dataset stored as an SQLite database file
B) A large, structured dataset stored in a Parquet format
C) A dataset consisting of unstructured text stored as plain .txt files
D) A small dataset with fewer than 1,000 rows stored in an Excel file

Solutions: