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Updated: Jul 05, 2026
No. of Questions: 303 Questions & Answers with Testing Engine
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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?
A) Hyperparameter tuning results.
B) Training dataset bias.
C) GPU utilization and memory usage.
D) Number of model parameters.
2. You are working on an MLOps workflow that loads a dataset into GPU memory for model training using RAPIDS cuDF. Before performing transformations, you want to verify that the dataset will fit into available GPU memory.
Which of the following methods provides the most accurate estimate of dataset memory consumption in a RAPIDS cudf.DataFrame?
A) cudf_df.memory_usage(deep=True).sum()
B) cudf_df.__sizeof__()
C) cudf_df.to_pandas().memory_usage(deep=True).sum()
D) cudf_df.memory_usage().sum()
3. 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) Utilize multi-GPU training to parallelize the workload, reducing training time.
C) Optimize GPU performance by limiting the number of threads running on each GPU.
D) Use a single cloud instance with the largest GPU available to ensure maximum performance.
E) Use larger batch sizes to make better use of GPU memory during model training.
4. Which feature of NVIDIA MLFlow integration with Triton Inference Server allows for the seamless deployment and monitoring of models in production?
A) MLFlow handles only the training and not the deployment of models.
B) MLFlow allows for version control and automated model rollout.
C) MLFlow prevents models from being updated once deployed to production.
D) MLFlow only supports deployment on CPUs.
5. You are working with a cuDF DataFrame and need to convert a column named sales from float64 to int32 to save memory.
Which of the following is the correct and most efficient way to perform this conversion in cuDF?
A) df['sales'].convert_dtypes('int32')
B) df['sales'] = df['sales'].astype('int32')
C) df['sales'] = df['sales'].to_numeric('int32')
D) df['sales'].apply(lambda x: int(x))
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: B,E | Question # 4 Answer: B | Question # 5 Answer: B |
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