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

2. A data scientist is working with an imbalanced dataset in a fraud detection project. The dataset contains 1 million transactions, but only 2% of them are labeled as fraudulent. To improve the performance of the model, the scientist decides to generate synthetic data using NVIDIA RAPIDS cuDF.
Which of the following approaches is the best way to generate synthetic samples while preserving data characteristics?

A) Apply cuML.SMOTE() to generate synthetic samples based on the minority class distribution.
B) Use cudf.DataFrame.append(cudf.DataFrame.random()) to create new fraudulent transactions.
C) Use cudf.DataFrame.interpolate(method='linear') to create new fraudulent samples by interpolating between existing ones.
D) Use cudf.DataFrame.sample(frac=0.5, replace=True) to oversample the minority class.

3. You need to process a dataset containing 10 billion rows, applying complex transformations and aggregations. The dataset does not fit into RAM, and you need an efficient, scalable solution for parallel processing.
Which of the following is the best choice?

A) Pandas
B) Vaex
C) PySpark
D) Dask

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

5. You are preparing a dataset for training a machine learning model using NVIDIA RAPIDS cuML. The dataset contains a feature representing timestamps in nanoseconds.
To optimize GPU performance while ensuring precision, which data type should you choose?

A) bool - Provides a highly efficient way to store timestamps as binary values.
B) object - Allows flexibility in storing timestamps as strings for easier parsing.
C) int32 - Uses less memory and can store high-precision timestamps efficiently.
D) datetime64[ns] - Optimizes storage and computation for timestamp data in RAPIDS.

Solutions: