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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q231-Q236):

NEW QUESTION # 231
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 Triton Inference Server to distribute random forest model training across multiple GPUs.
C. NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.
D. NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.

Answer: D

NEW QUESTION # 232
You are training a deep learning model for image classification and want to optimize its hyperparameters, including learning rate, batch size, and number of layers.
Which of the following techniques is the most effective for efficiently searching through a high- dimensional hyperparameter space?

A. Random Search
B. Grid Search
C. Gradient Descent
D. Bayesian Optimization

Answer: D

NEW QUESTION # 233
You are a data scientist working on a large-scale deep learning project that requires significant computational resources. You have the option to run your workloads on a cloud-based GPU instance.
Which of the following statements best describes a key benefit of using cloud-based GPUs for your workload?

A. Cloud-based GPUs eliminate all data transfer bottlenecks and latencies when training models on large datasets.
B. Cloud-based GPUs provide consistent and predictable performance, identical to on-premise dedicated GPUs.
C. Cloud-based GPUs are always more cost-effective than on-premise GPUs, regardless of workload size and duration.
D. Cloud-based GPUs enable scalable resource allocation, allowing you to dynamically increase or decrease GPU instances as needed.

Answer: D

NEW QUESTION # 234
A data scientist is working on a dataset where the numerical features have different ranges, and they need to ensure uniformity across features before training a machine learning model.
Which of the following approaches, utilizing NVIDIA technologies, would best achieve this goal?

A. Use cuML's StandardScaler() to transform the features to have zero mean and unit variance.
B. Apply cuML's RobustScaler() to center the data using median and scale using the interquartile range.
C. Apply cuDF's normalize() function to scale each feature between 0 and 1.
D. Use cuML's PCA to directly remove the need for standardization by reducing dimensionality.

Answer: A

NEW QUESTION # 235
You are optimizing a deep learning model that runs on an NVIDIA GPU and notice that inference latency is unexpectedly high. You decide to use DLProf to analyze the model's execution profile. After running the profiler, you find that a significant portion of execution time is spent on a single GPU kernel.
Which of the following actions would best help you identify and optimize this performance bottleneck?

A. Modify the neural network architecture to use more convolutional layers, as this generally improves execution speed on NVIDIA GPUs.
B. Switch to a CPU-based execution environment, as it will eliminate any potential GPU bottlenecks.
C. Reduce the batch size to minimize the time spent on memory-bound operations and improve kernel efficiency.
D. Use DLProf's Tensor Core Analysis feature to determine if Tensor Cores are being utilized effectively.

Answer: D

NEW QUESTION # 236
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