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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?

A) NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.
B) NVIDIA Nsight Systems for managing dataset storage and retrieval performance.
C) NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.
D) NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.

2. You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?

A) Pandas with multiprocessing
B) Hadoop MapReduce
C) RAPIDS + Dask for distributed GPU-accelerated ETL
D) Apache Spark with its default CPU-based execution

3. You are training a large-scale random forest model on a dataset with millions of rows and hundreds of features. The training time is significantly high when using traditional CPU-based machine learning frameworks.
Which NVIDIA technology should you use to accelerate training while maintaining compatibility with common ML frameworks like scikit-learn?

A) NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.
B) NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.
C) NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.
D) NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.

4. A data scientist is training a deep learning model on an NVIDIA GPU-accelerated platform. The model is suffering from overfitting, leading to poor generalization on unseen data.
Which of the following techniques is the most effective for reducing overfitting in this scenario?

A) Removing data augmentation techniques
B) Reducing the learning rate
C) Applying dropout regularization
D) Increasing the number of layers in the model

5. You are training a machine learning model using RAPIDS cuML and need to ensure that all numeric features are standardized for better model performance.
Which of the following is the best approach for scaling data using RAPIDS?

A) df_scaled = (df - df.min()) / (df.max() - df.min())
B) df_scaled = df.apply(lambda x: x / np.linalg.norm(x))
C) df_scaled = df / df.max()
D) df_scaled = scaler.fit_transform(df)
E) scaler = cuml.preprocessing.StandardScaler()

Solutions: