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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are designing a reproducible benchmark to compare the performance of deep learning models across frameworks like PyTorch and TensorFlow using NVIDIA's A100 GPU.
Which step is most critical in ensuring fair benchmarking conditions?

A) Using a different precision setting for each framework to maximize performance per framework's capabilities.
B) Measuring only forward pass latency to compare inference speed while ignoring backward pass computation.
C) Ensuring the same CUDA/cuDNN and driver versions are installed when running benchmarks across frameworks.
D) Enabling XLA compiler optimizations only for TensorFlow to enhance its performance.

2. Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?

A) GPU utilization and memory usage.
B) Number of model parameters.
C) Training dataset bias.
D) Hyperparameter tuning results.

3. A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A) Using a single-threaded feature extraction approach to avoid overhead from parallelization.
B) Reducing dataset size by randomly removing data points without considering class balance.
C) Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.
D) Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.

4. You are processing a large dataset in a distributed computing environment using RAPIDS and Dask.
Your workflow involves frequent shuffling of data between partitions, leading to significant slowdowns.
Which of the following strategies is the best way to implement data caching to reduce shuffle overhead using NVIDIA technologies?

A) Disable caching altogether to force a recomputation of results, ensuring up-to-date data processing.
B) Use a CPU-based caching solution like Memcached to store intermediate data before reloading into cuDF.
C) Enable GPU-accelerated caching with RAPIDS cuDF and persist intermediate results in GPU memory.
D) Use traditional disk-based caching by writing intermediate results to CSV files and reloading when needed.

5. You are working with a large dataset using NVIDIA RAPIDS cuDF and need to normalize a numerical column (price) to scale its values between 0 and 1.
Which of the following approaches correctly normalizes the column using cuDF?

A) df["price"] = df["price"] / df["price"].max()
B) df["price"] = df["price"].applymap( 2. lambda x: (x - df["price"].min()) 3. / (df["price"].max() - df["price"].min()) 4. )
C) df["price"] = ( 2. df["price"] - df["price"].min() 3. ) / (df["price"].max() - df["price"].min())
D) df["price"] = (df["price"] - df["price"].mean()) / df["price"].std()

Solutions: