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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation	17%	- Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization - Data validation and quality assurance
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
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
Machine Learning	15%	- Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms - Model evaluation and validation - Distributed training strategies
Data Analysis	14%	- Distributed and parallel data processing - Data visualization and graph analytics - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA)
MLOps	19%	- Pipeline automation and orchestration - End-to-end workflow management - Monitoring, logging and maintenance - Model deployment and serving

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q257-Q262):

NEW QUESTION # 257
You are designing an ETL pipeline to process terabytes of financial transaction data in real time.
The pipeline consists of:
Extracting data from multiple sources (CSV, Parquet, and SQL databases), Transforming the data using operations such as filtering, joins, and aggregations, Loading the processed data into a data lake for analytics.
Given that you are using NVIDIA RAPIDS cuDF for GPU-accelerated ETL, which of the following approaches optimizes performance while ensuring scalability?

A. Use cuDF to read and process the data in batches, leveraging Dask-cuDF for distributed computation when necessary
B. Convert cuDF DataFrames to Pandas DataFrames before performing transformations for compatibility
C. Use CPU-based ETL frameworks such as Apache Spark without GPU acceleration
D. Load all data into a single, large cuDF DataFrame before performing transformations

Answer: A

NEW QUESTION # 258
You are setting up a GPU-accelerated data science environment that includes NVIDIA RAPIDS, PyTorch, TensorFlow, and other libraries for machine learning and data processing.
Given that these frameworks have different dependencies and version requirements, what is the best approach to avoid software conflicts while ensuring reproducibility across multiple environments?

A. Use Conda to create isolated virtual environments for each project and install dependencies via conda-forge or NVIDIA channels.
B. Install all packages globally using pip on the system-wide Python installation to ensure consistency.
C. Use a single Docker container with the latest versions of all dependencies installed system-wide.
D. Manually download and compile each library from source to guarantee compatibility across all versions.

Answer: A

NEW QUESTION # 259
Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?

A. TensorRT
B. NVIDIA A100
C. NVIDIA Tesla
D. NVIDIA Jetson

Answer: B

NEW QUESTION # 260
You are working on a financial fraud detection system using NVIDIA RAPIDS cuML. You have a large time-series dataset of transaction amounts over time, and you need to identify anomalies such as unusual spikes in transactions.
Which of the following approaches is the most appropriate for detecting anomalies using NVIDIA technologies?

A. Use cuML's DBSCAN clustering to identify anomalies based on density differences
B. Run standard Pandas-based anomaly detection methods on CPU for better precision
C. Train a deep learning model with TensorFlow and deploy it using NVIDIA Triton Inference Server for anomaly detection
D. Apply cuML's PCA-based anomaly detection to detect outliers in the principal component space

Answer: D

NEW QUESTION # 261
A data scientist is training a deep learning model on an NVIDIA GPU-accelerated platform. The model is suffering from overfitting, leading to poor generalization on unseen data.
Which of the following techniques is the most effective for reducing overfitting in this scenario?

A. Increasing the number of layers in the model
B. Reducing the learning rate
C. Removing data augmentation techniques
D. Applying dropout regularization

Answer: D

NEW QUESTION # 262
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