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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q176-Q181):

NEW QUESTION # 176
You are working on a data science project that involves processing large-scale financial transaction data. You want to optimize data manipulation operations using NVIDIA's RAPIDS cuDF.
Which of the following approaches best leverages NVIDIA technologies for efficient data manipulation?

A. Load the dataset into a Pandas DataFrame and use multi-threading to speed up operations.
B. Use cuDF DataFrames for data manipulation and rely on GPU-accelerated functions like .groupby(),
.merge(), and .applymap().
C. Convert the dataset into a SQLite database and execute SQL queries to perform data transformations.
D. Preprocess the data using Apache Spark's CPU-based DataFrame API before transferring it to a GPU for machine learning.

Answer: B

NEW QUESTION # 177
A machine learning engineer wants to accelerate the training of a large deep learning model while optimizing GPU utilization.
Which of the following strategies provides the best performance improvement using NVIDIA technologies?

A. Use CPU-based data augmentation techniques before transferring the data to the GPU for training.
B. Load all training data into GPU memory at once to minimize CPU-GPU data transfer time.
C. Disable mixed-precision training since it reduces numerical accuracy and does not significantly impact performance.
D. Use NVIDIA DALI (Data Loading Library) to preprocess and augment data on the GPU before feeding it to the model.

Answer: D

NEW QUESTION # 178
You have a large-scale dataset consisting of IoT sensor readings collected at one-minute intervals across multiple locations. The dataset contains missing values and requires scaling before applying a machine learning model. You plan to use NVIDIA RAPIDS to preprocess and analyze the time-series data efficiently on GPUs.
Which of the following preprocessing steps is the most efficient approach using NVIDIA RAPIDS?

A. Use pandas to fill missing values and scale the data, then convert it to cuDF for training.
B. Use Dask for distributed missing value imputation and train a model using TensorFlow's CPU-based estimator.
C. Use pandas for missing value imputation, then normalize the data using NumPy before converting to cuDF.
D. Use cuDF to handle missing values with GPU-accelerated interpolation and apply cuML's StandardScaler for feature scaling.

Answer: D

NEW QUESTION # 179
In the context of the CRISP-DM process, you are tasked with building a machine learning model on a large dataset. You decide to leverage GPU resources to accelerate training.
Which of the following steps in the CRISP-DM process are most likely to benefit from the use of GPU resources in the cloud environment? (Select two)

A. Data Understanding
B. Model Training and Evaluation
C. Business Understanding
D. Data Exploration
E. Model Deployment and Monitoring

Answer: B,D

NEW QUESTION # 180
You are developing an AI model for medical imaging that requires acquiring a large dataset of MRI scans from multiple sources.
Which NVIDIA technology would best assist in acquiring, standardizing, and efficiently handling the dataset?

A. Use NVIDIA Nsight Systems to collect MRI data and visualize potential performance bottlenecks.
B. Use NVIDIA Clara Imaging to standardize, curate, and preprocess MRI datasets for AI model training.
C. Use NVIDIA Morpheus to analyze MRI data for cybersecurity threats before storing it.
D. Use NVIDIA AI Enterprise to directly acquire and store MRI images from multiple hospitals.

Answer: B

NEW QUESTION # 181
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