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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are performing data cleansing on a large dataset using CuDF. The dataset contains numerical values, some of which are outliers. You need to remove or adjust these outliers to make your model training more robust.
Which of the following approaches should you consider for handling outliers efficiently in CuDF? (Select two)

A) Using applymap() to apply a custom function for handling outliers
B) Using dropna() to remove rows with outliers
C) Using clip() to set a maximum and minimum threshold for numerical values
D) Using quantile() to calculate the interquartile range (IQR) and filter out outliers

2. You are working with a dataset consisting of 100 million records stored in a distributed system. The dataset includes numerical and categorical variables, requiring both exploratory data analysis (EDA) and machine learning model training. The processing time using traditional CPU-based methods is too slow.
Which of the following techniques would be the most effective acceleration method to handle this workload efficiently?

A) Reduce the dataset to a smaller sample size before processin
B) Scale up to a high-core-count CPU machine
C) Store the dataset in a relational database and query it using SQL
D) Use RAPIDS cuDF for GPU-accelerated data processing

3. A machine learning engineer is tasked with optimizing an image classification model on a cloud platform. The engineer must select a GPU-accelerated instance that balances cost and performance while ensuring compatibility with frameworks like TensorFlow and PyTorch.
Which instance configuration is the most appropriate choice?

A) A CPU-only instance with 128 GB of RAM for increased data processing speed.
B) A single-core CPU instance with high disk I/O throughput for faster data loading.
C) A cloud instance with integrated graphics rather than dedicated NVIDIA GPUs.
D) A cloud instance with NVIDIA A100 GPUs and NVLink support.

4. You are working on a large-scale machine learning pipeline that involves processing massive datasets using multiple GPUs on an NVIDIA DGX system. You choose to use Dask to enable efficient parallel processing across multiple GPUs.
Which of the following steps is essential to correctly configure Dask for multi-GPU acceleration?

A) Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.
B) Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.
C) Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.
D) Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.

5. What is the primary advantage of using NVIDIA Triton Inference Server for deploying and monitoring machine learning models in production?

A) It provides GPU optimization to handle high-throughput inference workloads.
B) It only supports TensorFlow models for inference.
C) It is designed solely for edge devices and not for data centers.
D) It automatically tunes hyperparameters for all models.

Solutions: