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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You need to train a deep learning model using PyTorch on a dataset too large for a single GPU. You decide to use Dask with NVIDIA GPUs for multi-GPU scaling.
Which approach is the most effective for distributing the workload?

A) Use Dask Bag to shard the dataset and train separate PyTorch models on each shard
B) Use Dask-CUDA workers with PyTorch's DistributedDataParallel (DDP) for training across multiple GPUs
C) Use Dask.delayed to wrap PyTorch training functions and schedule them across multiple GPUs
D) Use Dask's built-in deep learning API to automatically distribute PyTorch models across GPUs

2. You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?

A) df = cudf.read_csv("data.parquet")
B) df = cudf.read_parquet("data.parquet")
C) df = pd.read_parquet("data.parquet")
D) df = cudf.to_gpu(pd.read_parquet("data.parquet"))

3. You are working with a large dataset on an NVIDIA GPU, where optimizing memory usage is a priority. Your dataset contains a column, transaction_id, which stores unique integer values ranging between 0 and 100,000.
Which of the following data types is the most memory-efficient choice for this column in cuDF?

A) df['transaction_id'] = df['transaction_id'].astype('int64')
B) df['transaction_id'] = df['transaction_id'].astype('int8')
C) df['transaction_id'] = df['transaction_id'].astype('float32')
D) df['transaction_id'] = df['transaction_id'].astype('int32')

4. A financial institution is developing an ETL pipeline to ingest and process large volumes of streaming data from various sources, including stock market feeds, real-time transactions, and economic indicators. The ETL process must be highly efficient to minimize latency while ensuring data integrity.
Which of the following strategies is best suited for implementing a high-performance, GPU-accelerated ETL pipeline?

A) Load data directly into an Excel spreadsheet and use VBA macros to clean and transform it.
B) Use Pandas and Python's built-in threading library to handle concurrent data ingestion and transformation.
C) Store all streaming data in a PostgreSQL database before performing batch transformations.
D) Utilize NVIDIA Morpheus with RAPIDS to preprocess real-time streaming data using GPU acceleration.

5. A company is deploying an MLOps pipeline for training and serving deep learning models. The data scientists want to leverage GPU acceleration at multiple stages of the pipeline to enhance efficiency.
Which of the following steps would benefit the most from GPU acceleration?

A) Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.
B) Model monitoring by logging metadata and performance metrics in a database.
C) Storing and retrieving models from a centralized object storage system.
D) Training and inference workloads using deep learning models with TensorFlow or PyTorch.

Solutions: