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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. After profiling a deep learning model using NVIDIA DLProf, you notice that a specific GEMM (General Matrix Multiplication) operation takes significantly longer than expected. The profiler output reveals that tensor cores are underutilized despite having an Ampere-based GPU with Tensor Cores enabled.
Which of the following actions is the MOST appropriate to improve performance?

A) Switch from stochastic gradient descent (SGD) to Adam optimizer, as Adam improves convergence and computational efficiency.
B) Convert the model's data type to float16 or bfloat16 and re-run the training with automatic mixed precision (AMP).
C) Disable CUDA graphs and enforce PyTorch's eager execution mode to improve kernel execution order.
D) Increase the batch size to maximize GPU memory usage and reduce kernel launch overhead.

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) Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.
D) Rely on Matplotlib to generate static plots and update them every minute with a loop.

3. You are working with a large dataset containing millions of high-resolution images for a deep learning project. The dataset needs to be processed efficiently on a GPU before training a model.
Which NVIDIA technology is best suited for preprocessing, augmenting, and efficiently loading the dataset into memory?

A) NVIDIA DALI (Data Loading Library) to accelerate data loading and preprocessing on the GPU.
B) NVIDIA RAPIDS cuDF to transform image data into tabular format for analysis.
C) NVIDIA Triton Inference Server to preprocess the dataset before model training.
D) NVIDIA Nsight Compute to optimize image dataset processing.

4. You are processing a large dataset with UNIX timestamps (seconds since Jan 1, 1970) ranging from Jan 1, 2000, to the present.
What is the most memory-efficient data type for the timestamp column in a GPU-accelerated cloud environment?

A) df['timestamp'] = df['timestamp'].astype('int32')
B) df['timestamp'] = df['timestamp'].astype('float32')
C) df['timestamp'] = df['timestamp'].astype('datetime64[ms]')
D) df['timestamp'] = df['timestamp'].astype('int64')

5. You are working on a data science project that involves processing large-scale financial transaction data. You want to optimize data manipulation operations using NVIDIA's RAPIDS cuDF.
Which of the following approaches best leverages NVIDIA technologies for efficient data manipulation?

A) Convert the dataset into a SQLite database and execute SQL queries to perform data transformations.
B) Load the dataset into a Pandas DataFrame and use multi-threading to speed up operations.
C) Use cuDF DataFrames for data manipulation and rely on GPU-accelerated functions like .groupby(),
.merge(), and .applymap().
D) Preprocess the data using Apache Spark's CPU-based DataFrame API before transferring it to a GPU for machine learning.

Solutions: