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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are analyzing a time-series dataset that represents temperature readings from an industrial sensor. Your goal is to detect anomalies that may indicate sensor failures or environmental changes.
Which of the following methods would be the most appropriate statistical technique for detecting anomalies in this time-series dataset?

A) One-Hot Encoding
B) Moving Average Smoothing
C) Principal Component Analysis (PCA)
D) Z-score (Standard Score) Analysis

2. A data scientist is training a deep learning model on an NVIDIA GPU but notices that the training speed is not significantly faster than when using a CPU.
Which of the following strategies is the best approach to fully utilize GPU acceleration and optimize training performance?

A) Using mixed precision training with NVIDIA Tensor Cores
B) Disabling cuDNN optimizations to allow more flexibility in kernel execution
C) Increasing the batch size beyond the GPU's memory limit
D) Using only CPU-based data augmentation to keep the GPU dedicated to training

3. In the context of the CRISP-DM process, you are tasked with building a machine learning model on a large dataset. You decide to leverage GPU resources to accelerate training.
Which of the following steps in the CRISP-DM process are most likely to benefit from the use of GPU resources in the cloud environment? (Select two)

A) Business Understanding
B) Data Exploration
C) Data Understanding
D) Model Deployment and Monitoring
E) Model Training and Evaluation

4. Which of the following is the most appropriate way to perform large-scale data processing in a GPU- accelerated environment using NVIDIA RAPIDS?

A) Use Dask on top of RAPIDS for distributed computing across multiple GPUs.
B) Use pandas for all data manipulations and rely on multi-threading for parallel execution.
C) Use NumPy exclusively for processing large datasets on GPUs.
D) Use TensorFlow for all data manipulations in a GPU environment.

5. You are training a machine learning model using NVIDIA RAPIDS cuML and notice that the training process is significantly slower than expected. You suspect that there are bottlenecks in data movement and computation.
Which of the following techniques can best help you diagnose and resolve these bottlenecks?

A) Use cuml.common.device_auto_mem_size() to check GPU memory usage and adjust batch sizes accordingly.
B) Move all data from GPU memory to CPU memory before training the model.
C) Use cudf.DataFrame.to_pandas() to convert the dataset to a pandas DataFrame for analysis.
D) Reduce the number of features used in training without profiling the actual bottlenecks.

Solutions: