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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- CRISP-DM and data science methodology - Cloud GPU environments and deployment - Resource management and scaling strategies - GPU architecture and acceleration principles
Data Analysis	14%	- Time-series analysis and anomaly detection - Distributed and parallel data processing - Data visualization and graph analytics - Exploratory Data Analysis (EDA)
Data Manipulation and Software Literacy	19%	- GPU-accelerated ETL workflows - Dependency management and containerization - Performance profiling and optimization tools - Data processing libraries selection and usage
Machine Learning	15%	- Model evaluation and validation - Model training and hyperparameter tuning - Distributed training strategies - GPU-accelerated ML frameworks and algorithms
Data Preparation	17%	- Workflow monitoring and bottleneck identification - Feature engineering and data type optimization - Data cleaning, preprocessing and transformation - Data validation and quality assurance
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 (Q207-Q212):

NEW QUESTION # 207
You are managing a data processing pipeline that utilizes NVIDIA RAPIDS on GPUs for accelerated data transformations. During execution, you notice that the pipeline is not achieving expected performance gains.
What is the most effective approach to monitor and diagnose bottlenecks in this pipeline using NVIDIA technologies?

A. Use NVIDIA Nsight Systems to profile kernel execution times and memory transfers.
B. Reduce the dataset size and rerun the pipeline without profiling tools to check for performance improvements.
C. Enable RAPIDS memory pool logging to check for memory fragmentation and out-of-memory errors.
D. Run the pipeline on CPU instead of GPU to compare execution times.

Answer: A

NEW QUESTION # 208
You are selecting a dataset for GPU-accelerated data science using NVIDIA RAPIDS.
Which dataset characteristic is most suitable for taking advantage of GPU acceleration?

A. A large, structured dataset stored in a Parquet format
B. A small dataset with fewer than 1,000 rows stored in an Excel file
C. A dataset consisting of unstructured text stored as plain .txt files
D. A dataset stored as an SQLite database file

Answer: A

NEW QUESTION # 209
A data science team wants to deploy a GPU-accelerated pipeline using cuGraph to analyze graph data on cloud infrastructure. They are evaluating different cloud-based GPU solutions.
Which of the following factors should they consider when selecting a cloud-based GPU instance for running cuGraph efficiently?

A. The choice of GPU instance does not affect cuGraph performance since all GPUs execute graph algorithms at the same speed.
B. cuGraph runs equally well on CPU-based virtual machines, making GPU instances unnecessary.
C. Cloud-based GPUs are only useful for rendering graphics, not for running cuGraph algorithms.
D. The availability of NVIDIA CUDA-enabled GPUs, as cuGraph requires CUDA for acceleration.

Answer: D

NEW QUESTION # 210
A machine learning team has deployed a fraud detection model in production, and they want to monitor its performance over time to detect model drift and performance degradation.
Which monitoring strategy is most effective for this use case?

A. Disabling logging to reduce system overhead and avoid storage costs
B. Logging only inference latency without tracking model predictions
C. Implementing model performance tracking with drift detection metrics
D. Using a static dataset for monitoring instead of real-time production data

Answer: C

NEW QUESTION # 211
You are working with a large dataset containing customer transactions and want to perform exploratory data analysis (EDA) efficiently. Given the dataset's size, you decide to use NVIDIA RAPIDS to accelerate the process.
Which of the following approaches is the most effective for conducting EDA using NVIDIA technologies?

A. Load the dataset into Pandas and use Matplotlib for visualization
B. Use RAPIDS cuDF to perform fast dataframe operations and visualize results with cuXfilter
C. Use TensorFlow and Keras to preprocess the data before performing EDA
D. Perform SQL queries on a CPU-based database for initial data analysis before GPU acceleration

Answer: B

NEW QUESTION # 212
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