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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?

A) Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage.
B) Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
C) Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
D) Store all data as CSV files and perform ETL operations using traditional row-based processing.

2. You have a massive time-series dataset containing millions of records per day, and you need to perform forecasting at scale.
Which of the following techniques best utilizes NVIDIA technologies to optimize time-series forecasting?

A) Convert time-series data to tensors and train a deep learning model using PyTorch with mixed precision on an NVIDIA GPU.
B) Run Facebook Prophet on a multi-core CPU environment to leverage parallel forecasting.
C) Use RAPIDS cuML's XGBoost-GPU implementation to forecast time-series patterns with improved training efficiency.
D) Use RAPIDS cuDF and Dask-cuDF to preprocess the data and train an LSTM model with PyTorch Lightning on multiple GPUs.

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

4. You have a pandas DataFrame with a column containing floating-point numbers, but it takes up too much memory. You want to convert it into a lower-precision type using CuDF or pandas while ensuring computational efficiency.
Which function would you use?

A) df.convert_dtypes()
B) df['col'].apply(lambda x: np.float16(x))
C) df.to_float16()
D) df.astype('float16')

5. A machine learning engineer is benchmarking a GPU-accelerated pipeline for data preprocessing and training. The pipeline consists of large-scale data transformations using RAPIDS cuDF and training a model with TensorFlow. The engineer wants to ensure that GPU utilization is maximized throughout the workflow.
Which approach is most effective in achieving this?

A) Use a single GPU to avoid overhead associated with multi-GPU synchronization.
B) Pipeline execution should be asynchronous, overlapping data preprocessing with model training.
C) Reduce the dataset size to fit entirely in GPU memory, even if it leads to data loss.
D) Precompute all features on the CPU before loading them into the GPU for training.

Solutions: