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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a dataset containing hundreds of millions of records, and you need to perform ETL operations such as filtering, joins, and aggregations. Given the dataset size, which NVIDIA- accelerated library should you use to achieve optimal performance?

A) Pandas, as it is widely used and supports all common DataFrame operations, even for very large datasets.
B) cuDF, as it provides GPU-accelerated DataFrame operations similar to Pandas, allowing for efficient processing of large datasets.
C) NumPy, because it is optimized for numerical computing and offers better performance for handling tabular data.
D) cuPy, because it provides GPU-accelerated array operations, making it the best option for processing tabular data.

2. A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?

A) Precompute the time-series visualization with Dask and display it in a static HTML page.
B) Rely on Matplotlib to generate static plots and update them every minute with a loop.
C) Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.
D) Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.

3. You are analyzing a dataset containing millions of transaction records to detect fraudulent transactions. The dataset is highly imbalanced, with fraudulent cases making up only 0.5% of the data.
Which data analysis strategy will most effectively handle the imbalance while minimizing the risk of overfitting?

A) Random undersampling of the majority class
B) SMOTE (Synthetic Minority Over-sampling Technique)
C) Stratified random sampling
D) Random oversampling of the minority class

4. You are working with a large dataset in NVIDIA RAPIDS cuDF and notice that the data processing pipeline is taking longer than expected.
Which of the following tools or techniques can help you identify and analyze bottlenecks in the pipeline?

A) Use nvtx.annotate() to label sections of code and analyze performance in Nsight Systems.
B) Enable pandas compatibility mode in cudf to improve performance.
C) Use cudf.DataFrame.astype(str) to convert numerical values to strings for easier debugging.
D) Use cuml.preprocessing.StandardScaler() to normalize the dataset before processing.

5. You are processing a dataset with billions of records and want to encode a categorical column efficiently using NVIDIA RAPIDS.
Which of the following methods correctly encodes categorical data using cuDF?

A) df['category_column'] = df['category_column'].apply(lambda x: hash(x) % 1000)
B) df['category_column'] = df['category_column'].astype('category')
C) df['category_column'] = LabelEncoder().fit_transform(df['category_column'])
D) df['category_column'] = df['category_column'].one_hot_encode()

Solutions: