NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?

A) Hadoop MapReduce
B) Apache Spark with its default CPU-based execution
C) RAPIDS + Dask for distributed GPU-accelerated ETL
D) Pandas with multiprocessing

2. A machine learning team has deployed a fraud detection model in production, and they want to monitor its performance over time to detect model drift and performance degradation.
Which monitoring strategy is most effective for this use case?

A) Implementing model performance tracking with drift detection metrics
B) Logging only inference latency without tracking model predictions
C) Using a static dataset for monitoring instead of real-time production data
D) Disabling logging to reduce system overhead and avoid storage costs

3. A data engineer is using cuDF in NVIDIA RAPIDS to generate a large synthetic dataset for machine learning training. The dataset consists of numerical and categorical features. The engineer needs to generate millions of rows efficiently while preserving the relationships between features.
Which of the following approaches is the most optimal?

A) Train a cuML KMeans model on the original data and use the cluster centroids as new synthetic data points.
B) Use cudf.Series.random() to create independent random values for each column separately.
C) Use cudf.to_pandas(), generate synthetic data using pandas and Scikit-learn, and then convert it back to cuDF.
D) Leverage cuML's PCA.inverse_transform() after fitting PCA to the original dataset to generate new synthetic samples.

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

5. A manufacturing company wants to detect anomalies in a real-time IoT sensor dataset using NVIDIA technologies. The data arrives as a continuous stream, and the company needs to process it efficiently for immediate anomaly detection.
Which NVIDIA-powered approach is the most suitable for this use case?

A) Apply cuML's Isolation Forest on incoming streaming data for real-time anomaly detection
B) Use RAPIDS cuDF with batch processing to analyze historical anomalies every few hours
C) Leverage cuGraph to model the sensor readings as a graph and detect anomalies using centrality measures
D) Deploy an Autoencoder-based deep learning model using NVIDIA TensorRT for real-time inference

Solutions: