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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

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

3. A data scientist is using RAPIDS cuML to build a predictive model on a large dataset containing numerical and categorical features.
To optimize feature engineering for accelerated GPU processing, which of the following is the best approach?

A) Normalize numerical features using min-max scaling implemented with cuDF, leveraging GPU acceleration.
B) Apply one-hot encoding on high-cardinality categorical features directly in cuDF without any transformation.
C) Convert all categorical variables to string format for efficient processing in cuDF.
D) Convert all numerical features to float64 to maximize precision for feature transformation.

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

5. You are tasked with designing an ETL workflow for a large-scale data processing pipeline using NVIDIA technologies. You need to ensure that the extraction, transformation, and loading phases are optimized for performance using hardware acceleration.
Which of the following NVIDIA technologies would be most suitable for accelerating the ETL process?

A) TensorFlow
B) RAPIDS
C) TensorRT
D) CUDA

Solutions: