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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Time-series analysis and anomaly detection - Data visualization and graph analytics - Distributed and parallel data processing - Exploratory Data Analysis (EDA)
Topic 2: Machine Learning	15%	- Distributed training strategies - Model training and hyperparameter tuning - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms
Topic 3: Data Preparation	17%	- Workflow monitoring and bottleneck identification - Data validation and quality assurance - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization
Topic 4: Data Manipulation and Software Literacy	19%	- GPU-accelerated ETL workflows - Performance profiling and optimization tools - Dependency management and containerization - Data processing libraries selection and usage
Topic 5: MLOps	19%	- Model deployment and serving - End-to-end workflow management - Pipeline automation and orchestration - Monitoring, logging and maintenance
Topic 6: GPU and Cloud Computing	16%	- GPU architecture and acceleration principles - Cloud GPU environments and deployment - CRISP-DM and data science methodology - Resource management and scaling strategies

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q230-Q235):

NEW QUESTION # 230
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. Load all data into a single, large cuDF DataFrame before performing transformations
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. Use cuDF to read and process the data in batches, leveraging Dask-cuDF for distributed computation when necessary

Answer: D

NEW QUESTION # 231
A company is deploying an MLOps pipeline for training and serving deep learning models. The data scientists want to leverage GPU acceleration at multiple stages of the pipeline to enhance efficiency.
Which of the following steps would benefit the most from GPU acceleration?

A. Model monitoring by logging metadata and performance metrics in a database.
B. Storing and retrieving models from a centralized object storage system.
C. Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.
D. Training and inference workloads using deep learning models with TensorFlow or PyTorch.

Answer: D

NEW QUESTION # 232
A data scientist is working on a customer segmentation model using NVIDIA RAPIDS on GPUs. The dataset contains millions of customer records with features such as transaction history, age, location, and frequency of visits.
To optimize feature engineering using NVIDIA technologies, what is the best approach?

A. Use cuDF to perform feature transformations like normalization and one-hot encoding directly on the GPU.
B. Convert all categorical variables into string representations to preserve their original format for later analysis.
C. Perform feature engineering on CPUs using pandas before transferring data to the GPU for training.
D. Store all numerical features in float64 format to prevent rounding errors during transformations.

Answer: A

NEW QUESTION # 233
You are comparing the performance of GPU-accelerated deep learning models on two cloud platforms: AWS EC2 and Google Cloud Platform (GCP). You want to design a benchmark that evaluates GPU resource utilization, processing time, and cost-efficiency for training models with large datasets.
Which actions should you take to implement an effective benchmark? (Select two)

A. Only consider the cost of GPU resources for each cloud provider without factoring in data storage and transfer costs.
B. Limit the benchmarking tests to a single model architecture for each cloud platform.
C. Run each model on the same GPU type and instance size across both cloud platforms.
D. Measure both GPU utilization and network latency between the cloud platform and your data storage location.

Answer: C,D

NEW QUESTION # 234
You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

A. Ensuring the ETL pipeline uses only batch processing for data ingestion.
B. Using a single storage location for both raw and transformed data to simplify the workflow.
C. Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
D. Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.
E. Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.

Answer: D,E

NEW QUESTION # 235
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