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

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You have deployed a deep learning model for image classification in a production environment, but inference latency is high. You need to optimize the model to reduce response time while maintaining accuracy.
Which NVIDIA technology is best suited for this task?

A) NVIDIA Clara Imaging to improve deep learning inference for image classification workloads.
B) NVIDIA TensorRT to optimize and accelerate deep learning inference by reducing model size and execution time.
C) NVIDIA DeepStream to process image classification models for low-latency inference in batch mode.
D) NVIDIA RAPIDS cuML to optimize deep learning inference using GPU-accelerated ML algorithms.

2. Which of the following is the main advantage of using TensorRT for inference in an accelerated data science pipeline?

A) TensorRT optimizes deep learning models to run efficiently on NVIDIA GPUs by reducing precision while maintaining accuracy.
B) TensorRT automatically builds training models from raw data without requiring pre-trained models.
C) TensorRT is mainly used for data visualization and not for model inference.
D) TensorRT is only compatible with image classification models and does not support other model types.

3. A machine learning team is handling large-scale datasets that need to be efficiently stored and accessed within an NVIDIA RAPIDS workflow.
Which of the following storage formats and techniques provides the best performance for GPU-based data science pipelines?

A) Store data in SQLite databases and query it into pandas before converting it to cuDF.
B) Store data in the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.
C) Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.
D) Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.

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

5. You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

A) df = (df - df.mean()) / df.std()
B) 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)
C) df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)
D) df = (df - df.min()) / (df.max() - df.min())

Solutions: