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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

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

3. You are working on a data science project where you need to process a large dataset containing
500 million records. You want to determine whether GPU acceleration would significantly improve performance.
Which of the following factors best indicates that you should use an accelerated computing solution like RAPIDS?

A) The dataset consists of simple arithmetic operations on a few columns and can be processed using vectorized NumPy operations.
B) The dataset is heavily structured but mainly requires text-based analysis using regex-based search and manipulation.
C) The dataset is a structured table with less than 100,000 records and can be handled efficiently with a Pandas DataFrame.
D) The dataset has high-dimensional sparse features and requires complex operations such as nearest neighbor search and clustering.

4. A data scientist is working with a dataset of sensor readings (temperature, pressure, vibration) in different scales and units. To ensure all features contribute equally to a machine learning model, the data needs to be standardized.
Which approach is best for standardizing numerical features?

A) Use Min-Max scaling to transform values into a fixed range (e.g., [0,1] or [-1,1]).
B) Apply z-score normalization (standardization) to scale values based on mean and standard deviation.
C) Apply log transformation to all numerical columns to force them into a uniform distribution.
D) Convert all numerical features to categorical values using binning.

5. You are working with cloud-based GPUs to process a large dataset (terabytes in size) stored in Parquet format. One column represents a unique identifier (e.g., product ID), and it contains only positive integers ranging from 1 to 100,000.
Which of the following data types provides the best balance of memory efficiency and performance?

A) float64
B) int8
C) float32
D) uint16

Solutions: