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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with large datasets in cuDF and have noticed significant performance bottlenecks due to repeated computation and excessive shuffling in your workflow. You want to use data caching to optimize the execution plan and reduce redundant operations.
Which of the following is the best way to implement data caching in cuDF to avoid repeated recomputation and excessive shuffling?

A) Store intermediate DataFrames as temporary CSV files on disk and reload them when needed to simulate a cache.
B) Use the .to_pandas() method to convert the cuDF DataFrame into a Pandas DataFrame, leveraging CPU caching instead of GPU caching.
C) Use the .reset_index() method after performing transformations to force materialization of the dataset and cache results.
D) Use the .persist() method on a cuDF DataFrame to store intermediate results in GPU memory, reducing redundant calculations.

2. You are working with a dataset containing billions of records stored in a Parquet file. You need to load this dataset efficiently into an NVIDIA-accelerated RAPIDS environment for feature engineering.
Which of the following is the best approach?

A) Use pandas.read_parquet() to load the dataset and then convert it to a cuDF DataFrame
B) Load the Parquet file into Dask and then convert it into a cuDF DataFrame for parallel processing
C) Load the Parquet file directly into a cuDF DataFrame using cudf.read_parquet()
D) Convert the dataset into a CSV format and use cudf.read_csv() to load it into RAPIDS

3. You are building a fraud detection system and have a dataset that includes a feature representing transaction amounts. The values range from a few cents to several thousand dollars.
What is the most appropriate data type for this feature?

A) Integer (rounding all transaction amounts to whole dollars).
B) Categorical (grouping transactions into bins such as "low", "medium", "high").
C) Boolean (True for transactions above $100, False otherwise).
D) Floating-point (with precision to store cents or fractions of a cent).

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

5. You are working on a data science project that requires augmenting a dataset using synthetic data.
You are utilizing cuDF and NVIDIA RAPIDS to speed up the data generation process.
Which of the following methods is the most effective way to generate synthetic data using cuDF in a RAPIDS workflow?

A) Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.
B) Use cuDF to manipulate data distributions and generate new data points based on existing features.
C) Use cudf.DataFrame.applymap() to create new synthetic features through complex mathematical functions.

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