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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Data validation and quality assurance - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization
Topic 2: Data Analysis	14%	- Distributed and parallel data processing - Exploratory Data Analysis (EDA) - Data visualization and graph analytics - Time-series analysis and anomaly detection
Topic 3: Data Manipulation and Software Literacy	19%	- Dependency management and containerization - Performance profiling and optimization tools - Data processing libraries selection and usage - GPU-accelerated ETL workflows
Topic 4: Machine Learning	15%	- Distributed training strategies - Model training and hyperparameter tuning - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms
Topic 5: GPU and Cloud Computing	16%	- Resource management and scaling strategies - Cloud GPU environments and deployment - CRISP-DM and data science methodology - GPU architecture and acceleration principles
Topic 6: MLOps	19%	- Monitoring, logging and maintenance - End-to-end workflow management - Model deployment and serving - Pipeline automation and orchestration

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q132-Q137):

NEW QUESTION # 132
You are tasked with optimizing the performance of an MLOps pipeline that uses GPU-accelerated workflows. After running initial benchmarks, you notice that the training time is higher than expected, despite the use of multiple GPUs.
What are the best strategies to optimize the GPU-accelerated workflow in this case? (Select two)

A. Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.
B. Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.
C. Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.
D. Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
E. Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.

Answer: B,D

NEW QUESTION # 133
You are training a convolutional neural network (CNN) model with a large dataset on a single GPU.
The model is consuming too much GPU memory, and training is slow.
Which of the following techniques would help you reduce GPU memory consumption while maintaining or improving the efficiency of training? (Select two)

A. Implement mixed precision training to lower memory usage and speed up computation without losing accuracy.
B. Use automatic differentiation for every operation to save memory during the backpropagation phase.
C. Use batch normalization to reduce memory usage by skipping some of the computations during training.
D. Use a smaller batch size to reduce the memory requirements per training iteration, allowing for faster training.
E. Decrease the size of the model by reducing the number of layers or the number of filters per layer.

Answer: A,D

NEW QUESTION # 134
You are preprocessing a dataset using NVIDIA RAPIDS cuDF and need to handle missing values in the column temperature by replacing them with the column's median value.
Which of the following approaches correctly achieves this in an optimized manner?

A. df['temperature'].fillna(df['temperature'].median(), inplace=True)
B. df['temperature'].dropna(inplace=True)
C. df['temperature'].fillna(df['temperature'].mean(), inplace=True)
D. 1. df['temperature'] = df['temperature'].map(2. lambda x: df['temperature'].median() if x is None else x
3.)

Answer: A

NEW QUESTION # 135
A financial services company is deploying an AI-driven risk assessment model using NVIDIA GPUs on a cloud platform. To optimize resource utilization and cost efficiency, they need to determine the best GPU deployment strategy.
Which of the following is the most effective approach?

A. Run all AI workloads on a single large GPU instance without any partitioning or workload separation.
B. Choose an on-demand cloud instance with an outdated GPU model to reduce costs, even if performance is compromised.
C. Deploy the model using NVIDIAAI Enterprise with MIG (Multi-Instance GPU) to allocate multiple workloads on a single GPU efficiently.
D. Deploy separate full-GPU instances for each workload, even if they have variable compute demands.

Answer: C

NEW QUESTION # 136
A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A. Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
B. Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
C. Manually generate random values using Python's built-in random module and load them into a cuDF DataFrame.
D. Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.

Answer: D

NEW QUESTION # 137
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