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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following is the most efficient method for processing big data in a distributed environment using NVIDIA technologies?

A) Relying on traditional CPU-only processing frameworks for big data tasks.
B) Using a single-node GPU-based solution for data processing.
C) Using cloud-based, non-GPU infrastructure for data processing
D) Utilizing a distributed data processing framework like Apache Spark with GPU acceleration from the NVIDIA RAPIDS library.

2. A data science team is deploying a deep learning model for real-time inference. The model is optimized for inference on an NVIDIA A100 GPU, but the team notices that inference latency is higher than expected.
Which of the following optimizations is most effective in reducing inference latency?

A) Enable CPU offloading to balance the workload between the CPU and GPU.
B) Reduce the model size by randomly pruning neurons without retraining.
C) Increase the batch size significantly to improve GPU utilization.
D) Use mixed-precision inference with TensorRT to accelerate computation.

3. You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?

A) df = cudf.to_gpu(pd.read_parquet("data.parquet"))
B) df = cudf.read_parquet("data.parquet")
C) df = cudf.read_csv("data.parquet")
D) df = pd.read_parquet("data.parquet")

4. You are comparing the performance of GPU-accelerated deep learning models on two cloud platforms: AWS EC2 and Google Cloud Platform (GCP). You want to design a benchmark that evaluates GPU resource utilization, processing time, and cost-efficiency for training models with large datasets.
Which actions should you take to implement an effective benchmark? (Select two)

A) Run each model on the same GPU type and instance size across both cloud platforms.
B) Limit the benchmarking tests to a single model architecture for each cloud platform.
C) Measure both GPU utilization and network latency between the cloud platform and your data storage location.
D) Only consider the cost of GPU resources for each cloud provider without factoring in data storage and transfer costs.

5. A data scientist is working with a large dataset containing millions of records and aims to accelerate the data preprocessing workflow using NVIDIA technologies.
Which of the following approaches is the most effective for optimizing data preprocessing performance using GPUs?

A) Using Dask to distribute processing across multiple CPU cores
B) Using RAPIDS cuDF to replace pandas operations
C) Using NumPy for array computations and parallelizing with Python's multiprocessing
D) Running data transformations in a multi-threaded CPU environment

Solutions: