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

2. A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?

A) Precompute the time-series visualization with Dask and display it in a static HTML page.
B) Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.
C) Rely on Matplotlib to generate static plots and update them every minute with a loop.
D) Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.

3. A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A) Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
B) Manually generate random values using Python's built-in random module and load them into a cuDF DataFrame.
C) Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
D) Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.

4. You are building a real-time recommendation system that processes high-frequency transactional data from millions of users.
The system must:
- Ingest and preprocess data efficiently
- Perform similarity computations for user-item recommendations
- Scale to handle rapid incoming transactions
Which of the following NVIDIA technologies is the best choice for this use case?

A) NVIDIA Triton Inference Server
B) CUDA Kernels with Custom C++ Code
C) NVIDIA NVTabular
D) RAPIDS cuGraph

5. You are working on a large dataset for a machine learning model and need to preprocess the data efficiently using NVIDIA RAPIDS cuDF on a GPU-accelerated system.
Which of the following statements is correct regarding data preparation using cuDF?

A) cuDF supports common Pandas-like operations, such as filtering, merging, and groupby, but performs them efficiently on the GPU.
B) cuDF can be used as a direct replacement for Pandas, but it is optimized for multi-node distributed computing rather than GPU acceleration.
C) cuDF requires all input data to be stored in Apache Parquet format, as it does not support CSV or JSON ingestion.
D) cuDF operations are executed lazily and require an explicit compute() call to execute transformations.

Solutions: