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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. 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) Dask DataFrame with Dask-CUDA
C) Spark with RAPIDS Accelerator
D) cuDF

2. You are working on an AI-driven customer behavior prediction project.
According to the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, what is the most critical task to complete during the Data Understanding phase?

A) Selecting the most appropriate machine learning algorithm for the prediction task.
B) Identifying and preparing feature engineering strategies to improve model accuracy.
C) Acquiring and exploring the dataset to assess quality, completeness, and potential biases.
D) Deploying the model into a production environment for real-time inference.

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

4. You are working with a cuDF DataFrame and need to convert a column named sales from float64 to int32 to save memory.
Which of the following is the correct and most efficient way to perform this conversion in cuDF?

A) df['sales'].apply(lambda x: int(x))
B) df['sales'].convert_dtypes('int32')
C) df['sales'] = df['sales'].to_numeric('int32')
D) df['sales'] = df['sales'].astype('int32')

5. You have trained a machine learning model using cuML as part of the Modeling phase in the CRISP- DM framework. Now, you need to assess how well the model performs before moving forward with deployment.
Which of the following steps aligns best with the Evaluation phase of CRISP-DM using NVIDIA technologies?

A) Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().
B) Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.
C) Deploy the model to an edge device using TensorRT for real-time inference.
D) Define the problem statement and collect relevant datasets before training the model.

Solutions: