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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a data processing pipeline using NVIDIA GPUs for accelerating computations.
You need to monitor the pipeline's performance to identify bottlenecks.
Which of the following tools or techniques can be used to efficiently recognize bottlenecks in such a GPU-accelerated pipeline? (Select two)

A) NVIDIA nvidia-smi
B) NVIDIA TensorRT Profiling
C) NVIDIA CUDA Profiler (nvprof)
D) NVIDIA DLA (Deep Learning Accelerator)
E) NVIDIA Nsight Systems

2. You are tasked with optimizing the performance of a large-scale data science project that involves deep learning models on a cloud infrastructure. Your organization is using GPUs for model training.
Which of the following strategies would be the most effective in optimizing GPU performance for data science tasks? (Select two)

A) Overclock the GPU to achieve higher computational speeds and improve training times.
B) Use a single cloud instance with the largest GPU available to ensure maximum performance.
C) Utilize multi-GPU training to parallelize the workload, reducing training time.
D) Use larger batch sizes to make better use of GPU memory during model training.
E) Optimize GPU performance by limiting the number of threads running on each GPU.

3. 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) Float64
B) String
C) Int32
D) Int64

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

5. A team is processing large-scale tabular data using cuDF and cuML on NVIDIA GPUs but is facing performance degradation.
Which of the following techniques would be the most effective in identifying and resolving bottlenecks in the pipeline?

A) Convert all datasets into pandas DataFrames for initial processing before moving to cuDF.
B) Use nvprof or nsight compute to analyze kernel execution time and memory transfer efficiency.
C) Reduce the dataset size to a smaller sample to speed up processing.
D) Increase GPU clock speed manually to force higher processing power.

Solutions: