NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. 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) Enable pandas compatibility mode in cudf to improve performance.
B) Use nvtx.annotate() to label sections of code and analyze performance in Nsight Systems.
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.

2. You are working on a large dataset for a machine learning model that will be trained using RAPIDS cuML. The dataset includes categorical, integer, and floating-point features.
Which of the following approaches is the best practice for determining the optimal data type choice for each feature using NVIDIA's RAPIDS cuDF library?

A) Use float16 for all floating-point data to reduce memory usage and increase GPU processing speed.
B) Convert categorical variables into int8 to optimize GPU memory usage.
C) Convert all numerical data to float64 for maximum precision in calculations.
D) Use float32 instead of float64 for floating-point numbers when possible, and leverage int8, int16, or int32 for categorical and integer data based on their range.

3. A data scientist is working with large-scale tabular datasets and wants to optimize data ingestion and storage for accelerated processing on NVIDIA GPUs. The scientist is considering different file formats and storage optimizations to maximize performance in a RAPIDS-based workflow.
Which of the following approaches is the most suitable for optimizing both storage and processing performance?

A) Load data directly into NumPy arrays before using RAPIDS cuDF for processing.
B) Store data in Parquet format and load it using cuDF in RAPIDS.
C) Convert datasets into CSV format and store them in local disk storage.
D) Use JSON format for easy readability and process it directly in cuDF.

4. You are analyzing a large financial dataset containing stock market tick-by-tick data stored in a cuDF DataFrame. Since the dataset contains billions of data points, you need to aggregate it at the minute level before visualizing price trends efficiently.
Which of the following is the best approach for aggregating and visualizing this time-series data using NVIDIA technologies?

A) Convert cuDF to Pandas, aggregate using .resample() in Pandas, and visualize using Matplotlib
B) Use cuML's TSNE function to reduce dimensionality before visualizing with Bokeh
C) Load the data into a relational database (e.g., PostgreSQL), run an SQL query for aggregation, and visualize using Seaborn
D) Use cuDF's .groupby() function to aggregate at the minute level, then visualize using hvPlot

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

Solutions: