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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is processing a dataset that is too large to fit into the memory of a single GPU. They decide to use Dask with cuDF to leverage multiple GPUs for accelerated computation.
Which of the following approaches ensures efficient parallelism when working with dask_cudf?

A) Use Dask futures to asynchronously submit GPU tasks and collect results when completed.
B) Convert the dataset into a Dask cuDF dataframe and use persist() to keep it in GPU memory.
C) Assign explicit chunks of data to each worker by manually dividing the dataset before passing it to Dask.
D) Use a single-threaded scheduler to avoid contention when running on multiple GPUs.

2. You are selecting a dataset for GPU-accelerated data science using NVIDIA RAPIDS.
Which dataset characteristic is most suitable for taking advantage of GPU acceleration?

A) A dataset stored as an SQLite database file
B) A small dataset with fewer than 1,000 rows stored in an Excel file
C) A large, structured dataset stored in a Parquet format
D) A dataset consisting of unstructured text stored as plain .txt files

3. You are tasked with implementing a multi-GPU data pipeline using Dask-CUDA to process large datasets stored in Parquet format. Your goal is to achieve optimal GPU memory utilization and minimize inter-GPU communication overhead.
Which of the following approaches best aligns with these goals?

A) Use dask.persist() instead of dask.compute() to force immediate execution of tasks before distribution to GPUs.
B) Use dask_cudf.read_parquet() with split_row_groups=True to evenly distribute data across GPUs.
C) Use dask.array instead of dask_cudf because it provides better performance for structured tabular data.
D) Set dask.config.set({'distributed.worker.memory.target': 0.9}) to allocate 90% of the total CPU memory for GPU operations.

4. 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

5. When scaling a distributed data processing framework using NVIDIA GPU technology for big data processing, which of the following factors is most critical to optimize performance?

A) Ensuring the proper configuration of GPU resources across all nodes in the distributed system.
B) Maximizing the amount of data transferred between GPUs for faster processing.
C) Using more CPU cores to handle computation-heavy tasks.
D) Limiting the number of GPU nodes used in the cluster to avoid complexity.

Solutions: