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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Feature engineering and data type optimization - Data validation and quality assurance - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification
Topic 2: Data Manipulation and Software Literacy	19%	- Data processing libraries selection and usage - Performance profiling and optimization tools - Dependency management and containerization - GPU-accelerated ETL workflows
Topic 3: Data Analysis	14%	- Distributed and parallel data processing - Exploratory Data Analysis (EDA) - Time-series analysis and anomaly detection - Data visualization and graph analytics
Topic 4: Machine Learning	15%	- Distributed training strategies - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning
Topic 5: GPU and Cloud Computing	16%	- GPU architecture and acceleration principles - Cloud GPU environments and deployment - Resource management and scaling strategies - CRISP-DM and data science methodology
Topic 6: 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 (Q280-Q285):

NEW QUESTION # 280
You are building an MLOps pipeline for a predictive model that uses tabular data with both categorical and numerical features.
To ensure efficient data processing and optimal model training on an NVIDIA GPU, which of the following data types would be most suitable for a categorical feature representing different product categories?

A. String
B. Int32
C. Float64
D. Int64

Answer: B

NEW QUESTION # 281
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. Using a static dataset for monitoring instead of real-time production data
B. Implementing model performance tracking with drift detection metrics
C. Logging only inference latency without tracking model predictions
D. Disabling logging to reduce system overhead and avoid storage costs

Answer: B

NEW QUESTION # 282
You need to generate synthetic data to augment an imbalanced dataset using RAPIDS™ and cuDF.
Which of the following strategies would be most effective in producing high-quality synthetic data for the minority class?

A. Create synthetic data by applying random transformations to the minority class, such as scaling, rotation, or flipping, using cuDF.
B. Use only the majority class data to train a model and generate synthetic data using a GAN (Generative Adversarial Network) in the RAPIDS ecosystem.
C. Use synthetic data generation libraries like SDV (Synthetic Data Vault) in conjunction with cuDF to create synthetic data that mimics the distribution of the minority class.
D. Generate synthetic data by duplicating entries from the minority class using cudf.DataFrame.sample().

Answer: C

NEW QUESTION # 283
A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.
The dataset contains:
A low-cardinality categorical feature (Product Type) with 10 unique values.
A high-cardinality categorical feature (User ID) with 100,000 unique values.
A numerical feature (Price) that requires transformation.
Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

A. Frequency encoding for User ID is an efficient alternative to one-hot encoding, as it replaces each category with its frequency in the dataset, reducing dimensionality while preserving useful information.
B. Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.
C. Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.
D. Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.
E. Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision.
F. Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.

Answer: D

NEW QUESTION # 284
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. Dask
B. PySpark
C. pandas
D. cuDF

Answer: D

NEW QUESTION # 285
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