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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following techniques are commonly used to identify and acquire datasets for machine learning and data science projects? (Select three)

A) Manual data entry
B) Web scraping
C) Data augmentation
D) Public APIs
E) Crowdsourcing

2. Which of the following is the most efficient way to implement data parallelism using Dask for multi- GPU scaling on an Nvidia platform?

A) Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.
B) Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.
C) Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.
D) Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.

3. A machine learning engineer wants to evaluate the performance of NVIDIA RAPIDS cuDF and Apache Spark for large-scale data processing on a GPU-enabled cluster.
Which of the following strategies is the most effective for obtaining a fair and comprehensive benchmark?

A) Focus only on processing speed without considering resource consumption differences between frameworks.
B) Run Spark on a CPU cluster while running RAPIDS on a GPU to compare real-world scenarios.
C) Limit the benchmark to small datasets since GPUs excel at parallel processing.
D) Execute identical ETL workflows on cuDF and Spark-RAPIDS and measure execution time and resource utilization.

4. You are working on a structured dataset of around 10GB and need to perform exploratory data analysis (EDA), feature engineering, and filtering operations efficiently using NVIDIA technologies. The dataset fits into a single GPU's memory.
Which data processing library should you use to achieve the best performance?

A) pandas
B) cuDF
C) Spark with RAPIDS Accelerator
D) Dask DataFrame with Dask-CUDA

5. A data scientist is using RAPIDS cuML to build a predictive model on a large dataset containing numerical and categorical features.
To optimize feature engineering for accelerated GPU processing, which of the following is the best approach?

A) Normalize numerical features using min-max scaling implemented with cuDF, leveraging GPU acceleration.
B) Apply one-hot encoding on high-cardinality categorical features directly in cuDF without any transformation.
C) Convert all categorical variables to string format for efficient processing in cuDF.
D) Convert all numerical features to float64 to maximize precision for feature transformation.

Solutions: