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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q274-Q279):

NEW QUESTION # 274
You have deployed a deep learning model for image classification in a production environment, but inference latency is high. You need to optimize the model to reduce response time while maintaining accuracy.
Which NVIDIA technology is best suited for this task?

A. NVIDIA Clara Imaging to improve deep learning inference for image classification workloads.
B. NVIDIA DeepStream to process image classification models for low-latency inference in batch mode.
C. NVIDIA TensorRT to optimize and accelerate deep learning inference by reducing model size and execution time.
D. NVIDIA RAPIDS cuML to optimize deep learning inference using GPU-accelerated ML algorithms.

Answer: C

NEW QUESTION # 275
You are tasked with processing a large dataset of 100 million records for a deep learning project using NVIDIA technologies. You need to determine the most efficient data processing library for this task to maximize performance and reduce processing time.
Which of the following libraries is best suited for this task?

A. cuDF
B. pandas
C. PySpark
D. Dask

Answer: A

NEW QUESTION # 276
You need to deploy a machine learning model on a GPU-equipped system. The GPU has 16GB of VRAM, and the model requires approximately 12GB of memory during inference. However, additional system processes and other applications consume 5GB of VRAM.
What would happen if you attempt to run inference without making any optimizations, and how should you resolve the issue?

A. Switching from a GPU to CPU inference will resolve memory issues without performance loss
B. The model will run successfully but with reduced performance due to memory fragmentation
C. The model will fail to run due to out-of-memory (OOM) errors, and using a smaller batch size can help reduce memory usage
D. The model will run without issues because 16GB of VRAM is sufficient for a 12GB model

Answer: C

NEW QUESTION # 277
A financial services company is using NVIDIA RAPIDS cuML to train a credit risk assessment model.
The dataset contains hundreds of numerical and categorical features, including loan amount, credit score, income, employment history, and previous loan defaults.
To optimize feature selection using NVIDIA technologies, which approach should they take?

A. Convert all numerical features into categorical variables to simplify the data and reduce the need for feature selection.
B. Use cuML's feature selection algorithms, such as Recursive Feature Elimination (RFE), to identify the most important predictors.
C. Avoid feature selection, as modern deep learning models are capable of automatically handling redundant and irrelevant features.
D. Keep all features in the dataset to ensure the model captures as much information as possible, even if some features are redundant.

Answer: B

NEW QUESTION # 278
You are tasked with comparing the performance of different GPU-accelerated frameworks for a deep learning model. The frameworks you are considering are TensorFlow, PyTorch, and CUDA. To evaluate the performance, you decide to implement a benchmark that measures GPU efficiency, memory usage, and speed.
Which of the following actions should you take to design an effective benchmark? (Select two)

A. Use a common dataset for all frameworks to ensure comparability.
B. Use CPU-based implementations of the same frameworks for comparison.
C. Measure GPU utilization and memory usage, but ignore the network and disk I/O.
D. Benchmark only the training phase of the deep learning model.
E. Use a batch size that is optimal for each framework's memory management.

Answer: A,E

NEW QUESTION # 279
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