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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are conducting rapid experimentation on an NVIDIA GPU to determine the best trade-off between model accuracy and inference latency.
Which approach is the most efficient for systematically evaluating multiple configurations?

A) Test different model configurations on a CPU first before moving to the GPU for final evaluation
B) Use automated hyperparameter tuning tools like Optuna or Ray Tune with mixed precision training
C) Reduce training epochs significantly to save time, even if the model is underfitting
D) Train each possible model variation from scratch to evaluate accuracy and performance differences

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

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

4. You are training a machine learning model using NVIDIA RAPIDS cuML and notice that the training process is significantly slower than expected. You suspect that there are bottlenecks in data movement and computation.
Which of the following techniques can best help you diagnose and resolve these bottlenecks?

A) Move all data from GPU memory to CPU memory before training the model.
B) Use cuml.common.device_auto_mem_size() to check GPU memory usage and adjust batch sizes accordingly.
C) Use cudf.DataFrame.to_pandas() to convert the dataset to a pandas DataFrame for analysis.
D) Reduce the number of features used in training without profiling the actual bottlenecks.

5. You are tasked with designing and implementing a benchmark to compare the performance of different deep learning frameworks, including TensorFlow, PyTorch, and JAX, using NVIDIA GPUs.
Which of the following is the most effective approach to ensure an accurate and fair comparison?

A) Ensure identical hardware configurations, dataset preprocessing, and model architectures while leveraging NVIDIA's Nsight Systems and DLProf for analysis.
B) Use mixed precision (FP16) training only in TensorFlow to maximize performance while keeping other frameworks at FP32.
C) Run each framework with default settings to compare their out-of-the-box performance without any optimizations.
D) Compare training times only without considering throughput, power efficiency, or memory utilization.

Solutions: