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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q51-Q56):

NEW QUESTION # 51
Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)

A. Using Excel to manipulate large datasets
B. Using Dask for parallel processing of large datasets
C. Using SQLAlchemy to interact with databases for large data processing
D. Using SQLite as a local database for large-scale data analysis
E. Using Apache Spark for distributed data processing

Answer: B,E

NEW QUESTION # 52
You are consulting for a retail company that collects data from daily sales transactions, customer interactions, and inventory tracking across multiple locations. They are unsure whether their dataset qualifies as big data and which processing method would be most suitable.
Which of the following characteristics best indicate that the dataset requires big data processing and acceleration techniques?

A. The dataset contains more than 1 million records, making it impossible to process using pandas
B. The dataset exceeds the memory capacity of a single machine and requires distributed or GPU- accelerated processing
C. The dataset is stored in multiple relational database tables, making querying inefficient
D. The dataset includes a mix of numerical and categorical variables, requiring additional preprocessing

Answer: B

NEW QUESTION # 53
You are tasked with acquiring a dataset for training a machine learning model in healthcare, predicting patient readmission rates. Before using the dataset, you must assess its quality.
Which of the following is the most important factor to evaluate before acquisition?

A. The number of missing values and inconsistencies in key columns
B. Whether the dataset is stored on a cloud server or a local machine
C. The programming language used to preprocess the dataset
D. The file format (CSV, JSON, or XML) of the dataset

Answer: A

NEW QUESTION # 54
You are working on an AI-driven customer behavior prediction project.
According to the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, what is the most critical task to complete during the Data Understanding phase?

A. Identifying and preparing feature engineering strategies to improve model accuracy.
B. Deploying the model into a production environment for real-time inference.
C. Selecting the most appropriate machine learning algorithm for the prediction task.
D. Acquiring and exploring the dataset to assess quality, completeness, and potential biases.

Answer: D

NEW QUESTION # 55
You are setting up a GPU-accelerated data science environment on a cloud-based instance that utilizes NVIDIA GPUs.
To ensure compatibility between CUDA, RAPIDS, and Python libraries, which of the following is the most effective approach for managing dependencies and avoiding version conflicts?

A. Install RAPIDS, PyTorch, TensorFlow, and other libraries in the same Conda base environment for simplified access
B. Use a virtual machine with all dependencies pre-installed to avoid any software conflicts
C. Use Conda environments with version-pinned dependencies to create an isolated environment for RAPIDS and CUDA
D. Manually install required libraries in a system-wide Python environment using pip install without version specifications

Answer: C

NEW QUESTION # 56
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