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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You have developed a deep learning model using TensorFlow and trained it on an NVIDIA A100 GPU. The model is deployed in production and serves real-time inference requests. However, the inference latency is high, and you need to optimize performance without retraining the model.
Which of the following approaches is the most effective for optimizing inference performance using NVIDIA technologies?

A) Convert the model to ONNX format and use TensorRT for inference optimization.
B) Reduce the batch size to decrease computational overhead and improve latency.
C) Implement data augmentation techniques to improve inference efficiency.
D) Enable mixed precision training and retrain the model to improve inference speed.

2. You are working with a large dataset in NVIDIA RAPIDS cuDF and notice that the data processing pipeline is taking longer than expected.
Which of the following tools or techniques can help you identify and analyze bottlenecks in the pipeline?

A) Use cuml.preprocessing.StandardScaler() to normalize the dataset before processing.
B) Enable pandas compatibility mode in cudf to improve performance.
C) Use nvtx.annotate() to label sections of code and analyze performance in Nsight Systems.
D) Use cudf.DataFrame.astype(str) to convert numerical values to strings for easier debugging.

3. When scaling data parallelism using Dask with multiple Nvidia GPUs, what is the key consideration to avoid memory issues when distributing large datasets?

A) Split the dataset into smaller partitions that fit into each GPU's memory to prevent out-of-memory errors, and let Dask manage data distribution.
B) Use dask_gpu instead of dask_cuda to manage memory automatically across GPUs.
C) Ensure that each GPU's memory usage is manually monitored and adjusted, as Dask does not manage memory allocation automatically across GPUs.
D) Allow Dask to allocate data chunks dynamically without partitioning the dataset first, letting the system handle memory distribution automatically.

4. A data scientist is training a deep learning model on an NVIDIA GPU and wants to profile the model to identify performance bottlenecks. The scientist chooses to use NVIDIA DLProf.
Which of the following steps is the most effective way to profile the model using DLProf?

A) Modify the training script to manually insert timing functions for each layer and compare execution times.
B) Use nvprof instead of DLProf since it provides more detailed profiling for deep learning workloads.
C) Rely on general CPU profiling tools like perf and gprof to analyze GPU performance.
D) Run the model using dlprof --mode profile to collect performance metrics and generate a report.

5. Which of the following hardware components is most critical for accelerating the training of deep learning models?

A) Random Access Memory (RAM)
B) Central Processing Unit (CPU)
C) Solid State Drive (SSD)
D) Graphics Processing Unit (GPU)

Solutions: