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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are deploying a deep learning model on an edge device with 8GB of available RAM. The model's estimated peak memory usage, including model weights, intermediate tensors, and batch data, is 9.5GB.
What is the best course of action to ensure successful deployment while maintaining performance?

A) Increase the device's swap space to compensate for insufficient RAM
B) Reduce the batch size during inference
C) Offload some computation to cloud-based processing
D) Reduce the number of model parameters by removing layers from the architecture

2. A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?

A) Prefetching data with the CPU while training on the GPU
B) Disabling data parallelism
C) Using mixed-precision training with Tensor Cores
D) Running training exclusively on CPU

3. You are tasked with implementing a multi-GPU data pipeline using Dask-CUDA to process large datasets stored in Parquet format. Your goal is to achieve optimal GPU memory utilization and minimize inter-GPU communication overhead.
Which of the following approaches best aligns with these goals?

A) Use dask_cudf.read_parquet() with split_row_groups=True to evenly distribute data across GPUs.
B) Use dask.array instead of dask_cudf because it provides better performance for structured tabular data.
C) Use dask.persist() instead of dask.compute() to force immediate execution of tasks before distribution to GPUs.
D) Set dask.config.set({'distributed.worker.memory.target': 0.9}) to allocate 90% of the total CPU memory for GPU operations.

4. In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A) Lower energy consumption compared to CPUs
B) Efficient handling of matrix operations in machine learning models
C) Faster parallel processing for large datasets
D) Lower cost of cloud infrastructure
E) Better for memory-intensive workloads

5. You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.
Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

A) df.memory_usage(deep=True).sum()
B) sys.getsizeof(df)
C) np.array(df).nbytes
D) df.info(memory_usage='deep')

Solutions: