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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are considering using a multi-GPU setup to accelerate training a large deep learning model.
Which of the following are important factors to consider when deciding whether to use single-GPU or multi-GPU training? (Select two)

A) Multi-GPU setups require proper load balancing and efficient gradient synchronization, as uneven distribution of work can lead to suboptimal performance.
B) The performance of multi-GPU training scales linearly with the number of GPUs, meaning adding more GPUs will always result in a proportional reduction in training time.
C) The increase in training time from using a multi-GPU setup is negligible, as the overhead from communication and synchronization is minimal.
D) The success of multi-GPU training depends heavily on the ability to increase the batch size without exceeding memory limits.

2. A company is processing large log files from a cloud application, accumulating over 5TB of data daily. The data processing pipeline must be GPU-accelerated to extract insights quickly.
Which of the following is the most effective approach to handle high-volume log processing using NVIDIA technologies?

A) Use cuDF with explicit memory management to load and process the entire dataset into a single GPU.
B) Leverage Dask-cuDF to distribute the dataset across multiple GPUs, ensuring efficient parallel processing.
C) Store logs as Pandas DataFrames and use multiprocessing to parallelize operations across CPU cores.
D) Use RAPIDS cuML for performing log file processing, taking advantage of its optimized ML algorithms.

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

4. A team is processing large-scale tabular data using cuDF and cuML on NVIDIA GPUs but is facing performance degradation.
Which of the following techniques would be the most effective in identifying and resolving bottlenecks in the pipeline?

A) Increase GPU clock speed manually to force higher processing power.
B) Convert all datasets into pandas DataFrames for initial processing before moving to cuDF.
C) Reduce the dataset size to a smaller sample to speed up processing.
D) Use nvprof or nsight compute to analyze kernel execution time and memory transfer efficiency.

5. A data engineer is tasked with processing a 5 TB dataset stored in Apache Parquet format. The dataset consists of user activity logs and needs to be filtered, aggregated, and processed for feature engineering before training an ML model. The engineer is deciding between Dask and Apache Spark.
Which statement best describes a key difference between the two frameworks?

A) Spark cannot utilize GPUs, whereas Dask has built-in GPU acceleration by default.
B) Dask is optimized for in-memory processing, while Spark requires disk-based storage for computation.
C) Spark is better suited for structured and semi-structured data, while Dask excels at unstructured data processing.
D) Dask is a more lightweight solution, often preferred for Python-centric workflows, whereas Spark provides broader ecosystem integrations and supports SQL-like operations natively.

Solutions: