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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is training a deep learning model on an NVIDIA GPU and wants to profile the model to identify performance bottlenecks. The scientist chooses to use NVIDIA DLProf.
Which of the following steps is the most effective way to profile the model using DLProf?

A) Use nvprof instead of DLProf since it provides more detailed profiling for deep learning workloads.
B) Rely on general CPU profiling tools like perf and gprof to analyze GPU performance.
C) Modify the training script to manually insert timing functions for each layer and compare execution times.
D) Run the model using dlprof --mode profile to collect performance metrics and generate a report.

2. You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.
Which approach best utilizes NVIDIA technologies for efficient forecasting?

A) Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
B) Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.
C) Use cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.
D) Use PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.

3. You are working with a large dataset in a cloud environment for a deep learning model. The dataset consists of several features including numerical values, categorical data, and timestamps.
Which of the following choices would result in the most efficient use of GPU and cloud resources when determining the optimal data type for each feature? (Select three)

A) Use int8 for categorical features where there are fewer than 256 categories.
B) Use float64 for all numerical features to ensure maximum precision.
C) Use int32 for all numerical features to save memory.
D) Use datetime64[ns] for timestamp features to ensure high precision.
E) Use object data types for categorical features to avoid type conversion overhead.

4. You are working with a large dataset on an NVIDIA GPU, where optimizing memory usage is a priority. Your dataset contains a column, transaction_id, which stores unique integer values ranging between 0 and 100,000.
Which of the following data types is the most memory-efficient choice for this column in cuDF?

A) df['transaction_id'] = df['transaction_id'].astype('int8')
B) df['transaction_id'] = df['transaction_id'].astype('int64')
C) df['transaction_id'] = df['transaction_id'].astype('int32')
D) df['transaction_id'] = df['transaction_id'].astype('float32')

5. You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A) Load the data into a Spark DataFrame and visualize using Apache Zeppelin
B) Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib
C) Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly
D) Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively

Solutions: