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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is analyzing a large dataset of financial transactions containing millions of records.
To efficiently perform exploratory data analysis (EDA) using RAPIDS cuDF, which approach provides the most optimized performance while ensuring comprehensive insights?

A) Downsample the dataset and analyze a subset using Pandas for efficiency.
B) Perform all analysis on the CPU to avoid potential GPU memory limitations.
C) Use RAPIDS cuDF functions like .describe() and .value_counts() to perform statistical summaries directly on the GPU.
D) Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.

2. A data scientist is analyzing sales data for an e-commerce company that experiences strong seasonal trends (e.g., increased sales during holiday seasons). The goal is to accurately forecast future sales using GPU-accelerated data science techniques.
Which approach would be the most effective?

A) Train a Decision Tree model using cuML to classify future sales trends.
B) Apply k-Means clustering to identify seasonal patterns and extrapolate future values.
C) Compute a rolling average and manually adjust for seasonality based on previous peak sales months.
D) Use a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and optimize it using NVIDIA RAPIDS.

3. You are working with a time-series dataset containing network traffic logs. You suspect the presence of anomalies, such as distributed denial-of-service (DDoS) attacks or sudden traffic surges.
Which machine learning approach is best suited for detecting these anomalies?

A) k-Nearest Neighbors (k-NN) for Classification
B) Autoencoder-based Anomaly Detection
C) Naive Bayes Classifier
D) Linear Regression

4. You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?

A) Convert both DataFrames to Pandas before performing the join for compatibility
B) Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining
C) Perform the join operation entirely in a CPU-based Spark environment for better stability
D) Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF

5. A data scientist is analyzing a large time-series dataset containing stock price movements of thousands of companies over a decade. The dataset is stored as a cuDF DataFrame and contains millions of rows. The scientist wants to visualize trends and patterns interactively while leveraging GPU acceleration.
Which of the following approaches is the most efficient for visualizing this time-series data?

A) Convert the cuDF DataFrame to Pandas and use matplotlib for plotting.
B) Use seaborn with a sampled subset of the dataset to generate line plots.
C) Use cuXfilter with a cuDF DataFrame to generate interactive visualizations directly on the GPU.
D) Use matplotlib with plt.plot() while applying df.to_pandas() to convert data.

Solutions: