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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. You are processing a large-scale transportation network graph using NVIDIA cuGraph. The graph is extremely large, consuming almost all available GPU memory. Performance is deteriorating, and some computations fail due to memory exhaustion.
What is the best approach to efficiently handle this large graph while keeping computations on the GPU?

A) Manually split the graph into chunks and process each chunk separately without any coordination.
B) Use cuGraph's multi-GPU support via Dask-cuGraph to distribute the graph across multiple GPUs.
C) Convert the graph into a NetworkX graph and process it on the CPU to reduce GPU memory usage.
D) Store the graph as a large Python dictionary and use cuGraph only for specific queries.

3. You are comparing the performance of NVIDIA RAPIDS cuML, TensorFlow, and PyTorch for training and inference on a dataset with millions of records.
To design a fair and effective benchmark, which approach should you take?

A) Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.
B) Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.
C) Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.

4. You are analyzing a large financial dataset containing stock market tick-by-tick data stored in a cuDF DataFrame. Since the dataset contains billions of data points, you need to aggregate it at the minute level before visualizing price trends efficiently.
Which of the following is the best approach for aggregating and visualizing this time-series data using NVIDIA technologies?

A) Convert cuDF to Pandas, aggregate using .resample() in Pandas, and visualize using Matplotlib
B) Use cuDF's .groupby() function to aggregate at the minute level, then visualize using hvPlot
C) Use cuML's TSNE function to reduce dimensionality before visualizing with Bokeh
D) Load the data into a relational database (e.g., PostgreSQL), run an SQL query for aggregation, and visualize using Seaborn

5. Which of the following scenarios are most appropriate for using GPU acceleration when working with large-scale datasets in machine learning? (Select two)

A) Training a large-scale natural language processing (NLP) model on text data with billions of words.
B) Running a deep learning model for image classification with millions of labeled images.
C) Performing exploratory data analysis (EDA) on a dataset of 100,000 rows with 10 features.
D) Using decision trees for a classification task with a dataset of 1 million rows and 20 features.
E) Running a large ensemble of simple models (e.g., random forests) on a dataset of 10 million rows.

Solutions: