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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q195-Q200):

NEW QUESTION # 195
In a typical MLOps pipeline, which of the following practices are essential to ensuring robust deployment and monitoring of machine learning models in production? (Select two)

A. Continuous integration and continuous deployment (CI/CD) pipelines for model updates.
B. Use of manual intervention for every model update to ensure accuracy.
C. Automated hyperparameter tuning during inference to optimize model performance.
D. Post-deployment data drift detection to assess model performance degradation.

Answer: A,D

NEW QUESTION # 196
You are training a deep learning model on a large dataset of images stored in an Amazon S3 bucket.
You want to optimize data loading, augmentation, and preprocessing on NVIDIA GPUs to avoid CPU bottlenecks.
Which of the following approaches is the most efficient for GPU-accelerated data preprocessing?

A. Load the dataset using PyTorch's torchvision.transforms and DataLoader, leveraging the CPU for data preprocessing and transferring batches to the GPU before training.
B. Use TensorFlow's tf.data API with tf.image transformations and ensure that the preprocessed images are transferred to GPU memory at the end of the pipeline.
C. Use OpenCV to load and preprocess images on the CPU, then transfer the processed images to the GPU before training.
D. Use NVIDIA DALI to decode images, apply transformations such as resizing and normalization, and load batches directly to the GPU for training.

Answer: D

NEW QUESTION # 197
You are working on a large-scale graph analysis problem that involves computing the shortest paths between nodes in a massive social network dataset. You decide to leverage NVIDIA RAPIDS cuGraph for accelerated computation.
Which of the following cuGraph functions should you use?

A. cugraph.sssp()
B. cugraph.k_truss()
C. cugraph.label_propagation()
D. cugraph.pagerank()

Answer: A

NEW QUESTION # 198
You are processing a dataset of high-resolution images for deep learning training and want to optimize image loading, augmentation, and transformation on an NVIDIA GPU.
Which of the following statements correctly describes the role of NVIDIA DALI (Data Loading Library) in accelerating data preparation?

A. NVIDIA DALI only supports preprocessing images and does not include any functionality for video or text data.
B. NVIDIA DALI can offload image preprocessing tasks to the GPU, reducing CPU bottlenecks and improving training throughput.
C. NVIDIA DALI does not support data augmentation and is only used for raw data loading.
D. NVIDIA DALI is an alternative to cuDF for GPU-accelerated tabular data processing.

Answer: B

NEW QUESTION # 199
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 RAPIDS cuDF to transform image data into tabular format for analysis.
B. NVIDIA Triton Inference Server to preprocess the dataset before model training.
C. NVIDIA DALI (Data Loading Library) to accelerate data loading and preprocessing on the GPU.
D. NVIDIA Nsight Compute to optimize image dataset processing.

Answer: C

NEW QUESTION # 200
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