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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Use cuGraph for graph analytics 2. Perform time series analysis and visualization 3. Detect anomalies in time series datasets
Data Preparation	17%	- Data Cleaning and Transformation 1. cuDF and pandas data preprocessing 2. Data normalization and standardization 3. Synthetic data generation with RAPIDS
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Benchmarking GPU workflows 2. CRISP-DM workflow execution 3. Docker and Conda environment management
MLOps	19%	- Deployment and Monitoring 1. Performance benchmarking and optimization 2. Model deployment in production environments 3. Memory and capacity evaluation
Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Distributed data processing frameworks (Dask) 2. Data caching and performance optimization 3. GPU-accelerated ETL design and implementation
Machine Learning	15%	- Model Development and Optimization 1. Memory optimization techniques (mixed precision, batching) 2. Hyperparameter tuning 3. Feature engineering 4. Multi-GPU training comparison

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q103-Q108):

NEW QUESTION # 103
You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?

A. Use Pandas directly for data manipulation and visualization.
B. Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.
C. Perform all EDA using NumPy and SciPy for optimized array computations.
D. Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU.

Answer: D

NEW QUESTION # 104
You are designing a data pipeline for a healthcare analytics company that processes petabytes of structured and semi-structured patient data. Your goal is to optimize performance while maintaining accuracy.
Which of the following strategies is the most appropriate choice for accelerating this workload?

A. Use RAPIDS cuDF and Dask-cuDF for distributed preprocessing and RAPIDS cuML for GPU- accelerated machine learning model training.
B. Load the data into a PostgreSQL database and run SQL queries for all transformations before training a model.
C. Use Apache Hadoop and MapReduce for preprocessing before training models on a single GPU.
D. Use RAPIDS cuDF for data ingestion, preprocessing, and transformation, then offload model training to scikit-learn on CPUs.

Answer: A

NEW QUESTION # 105
A data engineering team is tasked with processing terabytes of log data every hour using an ETL pipeline. Due to the large data volume, they need a scalable GPU-accelerated solution that can distribute data processing across multiple GPUs.
Which approach best meets their needs?

A. Use cuDF alone for processing log data, as it provides optimal performance on a single GPU.
B. Process data using Pandas, then export the results to a CSV file for GPU-accelerated analytics.
C. Use NumPy for data transformations before converting the dataset into cuDF for final storage.
D. Use Dask-cuDF to distribute cuDF DataFrame operations across multiple GPUs, enabling parallel ETL processing.

Answer: D

NEW QUESTION # 106
You are setting up a GPU-accelerated data science environment that includes NVIDIA RAPIDS, PyTorch, TensorFlow, and other libraries for machine learning and data processing.
Given that these frameworks have different dependencies and version requirements, what is the best approach to avoid software conflicts while ensuring reproducibility across multiple environments?

A. Use Conda to create isolated virtual environments for each project and install dependencies via conda-forge or NVIDIA channels.
B. Install all packages globally using pip on the system-wide Python installation to ensure consistency.
C. Use a single Docker container with the latest versions of all dependencies installed system-wide.
D. Manually download and compile each library from source to guarantee compatibility across all versions.

Answer: A

NEW QUESTION # 107
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. Categorical encoding (one-hot encoding or ordinal encoding).
C. Floating-point representation (e.g., 0.1 for Idle, 0.5 for Running, 0.9 for Error).
D. Text (storing states as raw strings like "Idle", "Running", "Error").

Answer: B

NEW QUESTION # 108
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