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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q71-Q76):

NEW QUESTION # 71
You are working on a predictive maintenance model for industrial equipment. The dataset includes various sensor readings, categorical metadata, and timestamped events.
Which data type is the best choice for a feature representing the operating status of a machine, which has three possible states: "Idle", "Running", and "Error"?

A. Integer (0, 1, 2) to represent each state numerically.
B. Floating-point representation (e.g., 0.1 for Idle, 0.5 for Running, 0.9 for Error).
C. Categorical encoding (one-hot encoding or ordinal encoding).
D. Text (storing states as raw strings like "Idle", "Running", "Error").

Answer: C

NEW QUESTION # 72
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.applymap() to create new synthetic features through complex mathematical functions.
B. Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.
C. Use cuDF to manipulate data distributions and generate new data points based on existing features.

Answer: C

NEW QUESTION # 73
A machine learning engineer runs NVIDIA DLProf to analyze the performance of a deep learning model and receives a report indicating high GPU idle time.
What is the most likely cause of this issue?

A. The batch size is too large, leading to excessive GPU memory utilization and slow processing.
B. The CUDA cores are overheating, leading to automatic throttling of computations.
C. The GPU is not powerful enough to process the deep learning model efficiently.
D. The model is experiencing data loading bottlenecks, causing the GPU to wait for input batches.

Answer: D

NEW QUESTION # 74
You are working with a deep learning model for real-time inference on an NVIDIA GPU. Your objective is to optimize inference speed while maintaining acceptable model accuracy.
Which of the following techniques provides the best balance between inference performance and accuracy?

A. Use double-precision (FP64) computations to prevent numerical instability in inference
B. Use TensorRT for model optimization, including precision quantization and layer fusion
C. Increase the number of neurons in each layer to improve the model's accuracy
D. Disable batch inference to ensure each sample is processed independently for better accuracy

Answer: B

NEW QUESTION # 75
A machine learning team is handling large-scale datasets that need to be efficiently stored and accessed within an NVIDIA RAPIDS workflow.
Which of the following storage formats and techniques provides the best performance for GPU-based data science pipelines?

A. Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.
B. Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.
C. Store data in the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.
D. Store data in SQLite databases and query it into pandas before converting it to cuDF.

Answer: C

NEW QUESTION # 76
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