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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. In a typical MLOps pipeline, which of the following practices are essential to ensuring robust deployment and monitoring of machine learning models in production? (Select two)

A) Continuous integration and continuous deployment (CI/CD) pipelines for model updates.
B) Post-deployment data drift detection to assess model performance degradation.
C) Automated hyperparameter tuning during inference to optimize model performance.
D) Use of manual intervention for every model update to ensure accuracy.

2. A company is processing large log files from a cloud application, accumulating over 5TB of data daily. The data processing pipeline must be GPU-accelerated to extract insights quickly.
Which of the following is the most effective approach to handle high-volume log processing using NVIDIA technologies?

A) Leverage Dask-cuDF to distribute the dataset across multiple GPUs, ensuring efficient parallel processing.
B) Use cuDF with explicit memory management to load and process the entire dataset into a single GPU.
C) Store logs as Pandas DataFrames and use multiprocessing to parallelize operations across CPU cores.
D) Use RAPIDS cuML for performing log file processing, taking advantage of its optimized ML algorithms.

3. A data scientist is using NVIDIA RAPIDS cuDF to process a large dataset of customer transactions.
The dataset contains numerical, categorical, and timestamp-based features.
To optimize memory usage and performance on NVIDIA GPUs, which approach should they take when selecting data types?

A) Convert all timestamp features into object (string) format to maintain readability and ensure compatibility with GPU processing.
B) Avoid downcasting integer columns, as lower-bit integer types (e.g., int8) are not supported in GPU- accelerated computations.
C) Convert categorical variables into cuDF categorical data types and downcast numerical columns to the smallest possible precision without losing information.
D) Store all numerical columns as float64 to preserve maximum precision, even if lower precision suffices.

4. You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?

A) NVIDIA Nsight Systems for managing dataset storage and retrieval performance.
B) NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.
C) NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.
D) NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.

5. A data scientist is training a deep learning model on an NVIDIA GPU but notices that the training speed is not significantly faster than when using a CPU.
Which of the following strategies is the best approach to fully utilize GPU acceleration and optimize training performance?

A) Using only CPU-based data augmentation to keep the GPU dedicated to training
B) Disabling cuDNN optimizations to allow more flexibility in kernel execution
C) Using mixed precision training with NVIDIA Tensor Cores
D) Increasing the batch size beyond the GPU's memory limit

Solutions: