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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are training a deep learning model for image classification and want to optimize its hyperparameters, including learning rate, batch size, and number of layers.
Which of the following techniques is the most effective for efficiently searching through a high- dimensional hyperparameter space?

A) Random Search
B) Gradient Descent
C) Bayesian Optimization
D) Grid Search

2. You are working with a large dataset in a GPU-accelerated environment, and one of the columns, revenue, contains numeric values representing the annual revenue for companies. The revenue values are in the billions of dollars.
Which of the following is the most memory-efficient data type for the revenue column in a cuDF DataFrame?

A) df['revenue'] = df['revenue'].astype('float64')
B) df['revenue'] = df['revenue'].astype('float32')
C) df['revenue'] = df['revenue'].astype('uint32')
D) df['revenue'] = df['revenue'].astype('int64')

3. You are working on an AI-driven customer behavior prediction project.
According to the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, what is the most critical task to complete during the Data Understanding phase?

A) Deploying the model into a production environment for real-time inference.
B) Identifying and preparing feature engineering strategies to improve model accuracy.
C) Selecting the most appropriate machine learning algorithm for the prediction task.
D) Acquiring and exploring the dataset to assess quality, completeness, and potential biases.

4. A data science team wants to leverage GPU acceleration for detecting anomalies in a massive IoT sensor dataset that is continuously streaming.
Which of the following NVIDIA-supported methods would be the best for handling real-time anomaly detection in a high-throughput environment?

A) Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.
B) Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.
C) Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.
D) Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.

5. You are developing an AI model for medical imaging that requires acquiring a large dataset of MRI scans from multiple sources.
Which NVIDIA technology would best assist in acquiring, standardizing, and efficiently handling the dataset?

A) Use NVIDIA Nsight Systems to collect MRI data and visualize potential performance bottlenecks.
B) Use NVIDIA AI Enterprise to directly acquire and store MRI images from multiple hospitals.
C) Use NVIDIA Morpheus to analyze MRI data for cybersecurity threats before storing it.
D) Use NVIDIA Clara Imaging to standardize, curate, and preprocess MRI datasets for AI model training.

Solutions: