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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You need to benchmark GPU-accelerated data science frameworks across both cloud-based and on-premise GPU setups.
Which of the following is the most effective strategy for ensuring consistent and reliable benchmarking results?

A) Using mixed cloud and on-premise GPU instances to test various hardware types and configurations.
B) Running benchmarks in isolated virtual machines to simulate real-world multi-tenant cloud environments.
C) Running each benchmark multiple times and averaging the results to account for variance in execution time.
D) Prioritizing synthetic benchmarks over real-world workloads to get a more controlled performance measurement.

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

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

3. Which of the following tools in the NVIDIA AI stack is specifically designed to accelerate the deployment of machine learning models for production by optimizing inference performance?

A) cuML
B) cuBLAS
C) Triton Inference Server
D) DLA (Deep Learning Accelerator)

4. You are managing a data processing pipeline that utilizes NVIDIA RAPIDS on GPUs for accelerated data transformations. During execution, you notice that the pipeline is not achieving expected performance gains.
What is the most effective approach to monitor and diagnose bottlenecks in this pipeline using NVIDIA technologies?

A) Enable RAPIDS memory pool logging to check for memory fragmentation and out-of-memory errors.
B) Use NVIDIA Nsight Systems to profile kernel execution times and memory transfers.
C) Run the pipeline on CPU instead of GPU to compare execution times.
D) Reduce the dataset size and rerun the pipeline without profiling tools to check for performance improvements.

5. You are building an MLOps pipeline for a predictive model that uses tabular data with both categorical and numerical features.
To ensure efficient data processing and optimal model training on an NVIDIA GPU, which of the following data types would be most suitable for a categorical feature representing different product categories?

A) Int64
B) String
C) Float64
D) Int32

Solutions: