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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. In Python, when working with large datasets using pandas, which of the following methods are best for improving performance and efficiency when applying operations on DataFrames? (Select two)

A) Using apply() function over DataFrame rows
B) Using for loops to apply operations row by row
C) Using map() function to apply a function element-wise
D) Using iterrows() for iterating through DataFrame rows
E) Using vectorized operations (e.g., element-wise arithmetic)

2. Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A) Min-Max Scaling
B) Log Transformation
C) Robust Scaling
D) Z-score Standardization

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

4. 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 = pd.read_parquet("data.parquet")
C) df = cudf.to_gpu(pd.read_parquet("data.parquet"))
D) df = cudf.read_parquet("data.parquet")

5. You are tasked with processing a large dataset using multiple GPUs to accelerate computation. You decide to use Dask to implement data parallelism with NVIDIA's RAPIDS framework to maximize GPU utilization.
Which of the following steps is essential for efficiently distributing the workload across multiple GPUs in Dask?

A) Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.
B) Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.
C) Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.
D) Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.

Solutions: