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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. A machine learning engineer is working with a 1 TB dataset stored in Apache Parquet format and wants to analyze the data for patterns before building a model. The engineer is considering various acceleration methods.
Which of the following approaches would be the best choice for efficient analysis?

A) Use a GPU-accelerated library such as RAPIDS cuDF to load and process the Parquet file efficiently.
B) Load the dataset into a relational database and query it using simple SQL statements.
C) Read the Parquet file line by line using Python's built-in file handling functions to save memory.
D) Convert the Parquet file to a Pandas DataFrame and perform analysis using Pandas functions.

3. 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) Hyperparameter tuning results.
C) Number of model parameters.
D) Training dataset bias.

4. You are tasked with designing an ETL workflow for a large-scale data processing pipeline using NVIDIA technologies. You need to ensure that the extraction, transformation, and loading phases are optimized for performance using hardware acceleration.
Which of the following NVIDIA technologies would be most suitable for accelerating the ETL process?

A) TensorRT
B) RAPIDS
C) TensorFlow
D) CUDA

5. You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?

A) Drop all rows containing missing values using Pandas before transferring data to the GPU
B) Convert the dataset to a NumPy array and manually replace missing values with the mean
C) Apply a deep learning-based imputation model before moving data to the GPU
D) Use fillna() with a fixed value on the GPU using cuDF

Solutions: