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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
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. Model deployment in production environments 2. Performance benchmarking and optimization 3. Memory and capacity evaluation
Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Multi-GPU training comparison 3. Feature engineering 4. Memory optimization techniques (mixed precision, batching)
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 Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. GPU-accelerated ETL design and implementation 3. Distributed data processing frameworks (Dask)
Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. Data normalization and standardization 3. cuDF and pandas data preprocessing

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q233-Q238):

NEW QUESTION # 233
You need to deploy a machine learning model on a GPU-equipped system. The GPU has 16GB of VRAM, and the model requires approximately 12GB of memory during inference. However, additional system processes and other applications consume 5GB of VRAM.
What would happen if you attempt to run inference without making any optimizations, and how should you resolve the issue?

A. Switching from a GPU to CPU inference will resolve memory issues without performance loss
B. The model will run without issues because 16GB of VRAM is sufficient for a 12GB model
C. The model will run successfully but with reduced performance due to memory fragmentation
D. The model will fail to run due to out-of-memory (OOM) errors, and using a smaller batch size can help reduce memory usage

Answer: D

NEW QUESTION # 234
A data scientist is working with a 50 TB dataset consisting of structured logs from IoT devices. The data needs to be cleaned, transformed, and aggregated before training a machine learning model.
Which of the following frameworks would be the most efficient choice for distributed data processing?

A. SQLite
B. Python multiprocessing module
C. Apache Spark with RAPIDS Accelerator
D. Pandas

Answer: C

NEW QUESTION # 235
You are working on a data science project using NVIDIA RAPIDS on a multi-GPU system.
To ensure reproducibility and avoid software versioning conflicts, which of the following is the best approach for managing dependencies?

A. Use a manually compiled CUDA installation alongside system-installed Python libraries to manage GPU dependencies.
B. Install all required packages globally on the system using pip install without a virtual environment.
C. Use a Conda environment with RAPIDS-compatible versions of libraries installed using conda install
-c rapidsai -c nvidia.
D. Avoid dependency management frameworks and rely on manual tracking of package versions using a text file.

Answer: C

NEW QUESTION # 236
A financial institution is developing an ETL pipeline to ingest and process large volumes of streaming data from various sources, including stock market feeds, real-time transactions, and economic indicators. The ETL process must be highly efficient to minimize latency while ensuring data integrity.
Which of the following strategies is best suited for implementing a high-performance, GPU-accelerated ETL pipeline?

A. Utilize NVIDIA Morpheus with RAPIDS to preprocess real-time streaming data using GPU acceleration.
B. Store all streaming data in a PostgreSQL database before performing batch transformations.
C. Use Pandas and Python's built-in threading library to handle concurrent data ingestion and transformation.
D. Load data directly into an Excel spreadsheet and use VBA macros to clean and transform it.

Answer: A

NEW QUESTION # 237
A company is processing large log files from a cloud application, accumulating over 5TB of data daily. The data processing pipeline must be GPU-accelerated to extract insights quickly.
Which of the following is the most effective approach to handle high-volume log processing using NVIDIA technologies?

A. Use cuDF with explicit memory management to load and process the entire dataset into a single GPU.
B. Store logs as Pandas DataFrames and use multiprocessing to parallelize operations across CPU cores.
C. Use RAPIDS cuML for performing log file processing, taking advantage of its optimized ML algorithms.
D. Leverage Dask-cuDF to distribute the dataset across multiple GPUs, ensuring efficient parallel processing.

Answer: D

NEW QUESTION # 238
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