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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q274-Q279):

NEW QUESTION # 274
A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

A. Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.
B. Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
C. Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
D. Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.

Answer: A

NEW QUESTION # 275
You are comparing the performance of NVIDIA RAPIDS cuML, TensorFlow, and PyTorch for training and inference on a dataset with millions of records.
To design a fair and effective benchmark, which approach should you take?

A. Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.
B. Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.
C. Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.

Answer: B

NEW QUESTION # 276
You are designing an ETL pipeline to process terabytes of financial transaction data in real time.
The pipeline consists of:
Extracting data from multiple sources (CSV, Parquet, and SQL databases), Transforming the data using operations such as filtering, joins, and aggregations, Loading the processed data into a data lake for analytics.
Given that you are using NVIDIA RAPIDS cuDF for GPU-accelerated ETL, which of the following approaches optimizes performance while ensuring scalability?

A. Use cuDF to read and process the data in batches, leveraging Dask-cuDF for distributed computation when necessary
B. Use CPU-based ETL frameworks such as Apache Spark without GPU acceleration
C. Convert cuDF DataFrames to Pandas DataFrames before performing transformations for compatibility
D. Load all data into a single, large cuDF DataFrame before performing transformations

Answer: A

NEW QUESTION # 277
A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?

A. Precompute the time-series visualization with Dask and display it in a static HTML page.
B. Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.
C. Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.
D. Rely on Matplotlib to generate static plots and update them every minute with a loop.

Answer: B

NEW QUESTION # 278
A machine learning team needs to process terabytes of image metadata stored in a distributed storage system. They want to leverage GPU acceleration to speed up preprocessing and transformation while ensuring efficient parallel access.
Which of the following approaches best aligns with NVIDIA's accelerated data science ecosystem?

A. Process metadata using PySpark on a CPU cluster before sending it to a GPU.
B. Store metadata in a traditional SQL database and query it using Pandas.
C. Use Dask-cuDF with Parquet files stored in an object storage system like S3.
D. Use SQLite for metadata storage and process it with RAPIDS cuDF.

Answer: C

NEW QUESTION # 279
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