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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A machine learning engineer is tasked with optimizing an image classification model on a cloud platform. The engineer must select a GPU-accelerated instance that balances cost and performance while ensuring compatibility with frameworks like TensorFlow and PyTorch.
Which instance configuration is the most appropriate choice?

A) A single-core CPU instance with high disk I/O throughput for faster data loading.
B) A cloud instance with integrated graphics rather than dedicated NVIDIA GPUs.
C) A cloud instance with NVIDIA A100 GPUs and NVLink support.
D) A CPU-only instance with 128 GB of RAM for increased data processing speed.

2. You are using RAPIDS and Dask-cuDF to process a large-scale ETL pipeline. The workflow involves multiple join and groupby operations, which are causing excessive shuffling.
How can you best optimize caching to reduce shuffle overhead?

A) Force every operation to be recomputed from the raw dataset to ensure accurate results.
B) Split the dataset into multiple smaller Pandas DataFrames and store them in memory for quick retrieval.
C) Cache data using Apache Arrow's in-memory format, but process all operations on CPU.
D) Use dask.persist() to store frequently accessed cuDF DataFrames in GPU memory, reducing recomputation and shuffle operations.

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

4. You have a pandas DataFrame with a column containing floating-point numbers, but it takes up too much memory. You want to convert it into a lower-precision type using CuDF or pandas while ensuring computational efficiency.
Which function would you use?

A) df['col'].apply(lambda x: np.float16(x))
B) df.to_float16()
C) df.astype('float16')
D) df.convert_dtypes()

5. You are working with a dataset containing hundreds of millions of records, and you need to perform ETL operations such as filtering, joins, and aggregations. Given the dataset size, which NVIDIA- accelerated library should you use to achieve optimal performance?

A) cuPy, because it provides GPU-accelerated array operations, making it the best option for processing tabular data.
B) Pandas, as it is widely used and supports all common DataFrame operations, even for very large datasets.
C) cuDF, as it provides GPU-accelerated DataFrame operations similar to Pandas, allowing for efficient processing of large datasets.
D) NumPy, because it is optimized for numerical computing and offers better performance for handling tabular data.

Solutions: