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

2. A financial institution is training a fraud detection model on a GPU-powered NVIDIA RAPIDS cuML pipeline. Their dataset includes customer ages, transaction amounts, merchant names, and transaction timestamps.
To optimize GPU memory usage while preserving accuracy, how should they store these features?

A) Keep customer ages in float16 format to reduce memory consumption, as integer types are less efficient in GPU operations.
B) Convert all numeric data to float64 for maximum precision, as rounding errors in lower precision could impact the model's accuracy.
C) Convert timestamps into UNIX epoch integers instead of using datetime64 for more efficient GPU computations.
D) Store transaction amounts as float32, customer ages as int8, merchant names as categorical, and timestamps as datetime64.

3. When scaling data parallelism using Dask with multiple Nvidia GPUs, what is the key consideration to avoid memory issues when distributing large datasets?

A) Allow Dask to allocate data chunks dynamically without partitioning the dataset first, letting the system handle memory distribution automatically.
B) Ensure that each GPU's memory usage is manually monitored and adjusted, as Dask does not manage memory allocation automatically across GPUs.
C) Split the dataset into smaller partitions that fit into each GPU's memory to prevent out-of-memory errors, and let Dask manage data distribution.
D) Use dask_gpu instead of dask_cuda to manage memory automatically across GPUs.

4. You are working with a dataset in a cloud-based GPU environment that contains a column country representing the country of origin for customers. The column contains only 10 unique country values, but the dataset has millions of rows.
Which of the following is the most memory-efficient approach to handle the country column in a cuDF DataFrame?

A) df['country'] = df['country'].astype('string')
B) df['country'] = df['country'].astype('object')
C) df['country'] = df['country'].astype('int32')
D) df['country'] = df['country'].astype('category')

5. A data scientist is working on a customer segmentation model using NVIDIA RAPIDS on GPUs. The dataset contains millions of customer records with features such as transaction history, age, location, and frequency of visits.
To optimize feature engineering using NVIDIA technologies, what is the best approach?

A) Use cuDF to perform feature transformations like normalization and one-hot encoding directly on the GPU.
B) Convert all categorical variables into string representations to preserve their original format for later analysis.
C) Store all numerical features in float64 format to prevent rounding errors during transformations.
D) Perform feature engineering on CPUs using pandas before transferring data to the GPU for training.

Solutions: