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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q219-Q224):

NEW QUESTION # 219
Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

A. Model Building
B. Business Understanding
C. Data Understanding
D. Data Preparation

Answer: B

NEW QUESTION # 220
You are working on a data science project that requires augmenting a dataset using synthetic data.
You are utilizing cuDF and NVIDIA RAPIDS to speed up the data generation process.
Which of the following methods is the most effective way to generate synthetic data using cuDF in a RAPIDS workflow?

A. Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.
B. Use cudf.DataFrame.applymap() to create new synthetic features through complex mathematical functions.
C. Use cuDF to manipulate data distributions and generate new data points based on existing features.

Answer: C

NEW QUESTION # 221
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. Apply cuML's RobustScaler() to center the data using median and scale using the interquartile range.
B. Use cuML's PCA to directly remove the need for standardization by reducing dimensionality.
C. Apply cuDF's normalize() function to scale each feature between 0 and 1.
D. Use cuML's StandardScaler() to transform the features to have zero mean and unit variance.

Answer: D

NEW QUESTION # 222
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. Generate synthetic data by duplicating entries from the minority class using cudf.DataFrame.sample().
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. Create synthetic data by applying random transformations to the minority class, such as scaling, rotation, or flipping, using cuDF.

Answer: C

NEW QUESTION # 223
You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?

A. Hadoop MapReduce
B. Pandas with multiprocessing
C. RAPIDS + Dask for distributed GPU-accelerated ETL
D. Apache Spark with its default CPU-based execution

Answer: C

NEW QUESTION # 224
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