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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are training a machine learning model using scikit-learn-like API on a dataset with millions of samples and thousands of features. You need to optimize both training time and inference speed using NVIDIA technologies.
Which solution is the most appropriate?

A) Use NVIDIA Triton Inference Server to train the model efficiently on a single GPU.
B) Use NVIDIA RAPIDS cuML for GPU-accelerated machine learning model training.
C) Use NVIDIA Magnum IO to optimize machine learning model parameters on the GPU.
D) Use NVIDIA Modulus to accelerate machine learning training and feature selection.

2. A financial analyst is working with an irregularly spaced time-series dataset containing cryptocurrency transactions. The timestamps are not evenly distributed, with some periods having dense data and others having sparse entries. The analyst wants to visualize the data efficiently using GPU acceleration.
What is the best preprocessing approach before visualization?

A) Convert the dataset to Pandas and use df.resample() to aggregate by fixed time intervals.
B) Resample the time-series to a fixed frequency using cuDF.resample() and fill missing values.
C) Sort the data by timestamp and drop all sparse regions using df.dropna().
D) Ignore the irregularity and plot the raw timestamps directly without any preprocessing.

3. You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?

A) Drop all missing values using .dropna() instead of filling them, as GPU memory is limited
B) Use cuDF's .fillna() method to replace missing values in GPU memory
C) Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution
D) Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF

4. You are working on a large-scale graph analysis project using NVIDIA cuGraph for accelerated computations. Your dataset consists of millions of nodes and edges representing social network interactions. You need to efficiently compute PageRank while minimizing memory usage.
Which of the following techniques would be the most effective?

A) Use cuGraph's sparse matrix representation to store graph data and perform computations efficiently.
B) Convert the graph into an adjacency list format and store it in Python dictionaries for faster lookups.
C) Use Pandas DataFrames to manage the graph structure and leverage Pandas indexing for efficient queries.
D) Manually unroll loops and precompute node ranks in a NumPy array before feeding them into cuGraph.

5. You are training a machine learning model using NVIDIA RAPIDS cuML and notice that the training process is significantly slower than expected. You suspect that there are bottlenecks in data movement and computation.
Which of the following techniques can best help you diagnose and resolve these bottlenecks?

A) Reduce the number of features used in training without profiling the actual bottlenecks.
B) Move all data from GPU memory to CPU memory before training the model.
C) Use cuml.common.device_auto_mem_size() to check GPU memory usage and adjust batch sizes accordingly.
D) Use cudf.DataFrame.to_pandas() to convert the dataset to a pandas DataFrame for analysis.

Solutions: