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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q168-Q173):

NEW QUESTION # 168
You are developing an end-to-end data pipeline that processes terabytes of image metadata using NVIDIA technologies. You need a software stack that efficiently integrates GPU-accelerated data processing, machine learning, and visualization.
Which of the following tool combinations is best suited for this task?

A. Pandas for data manipulation, XGBoost for machine learning, and Matplotlib for visualization.
B. Hadoop for data storage, NumPy for computations, and TensorFlow for visualization.
C. Excel for data analysis, scikit-learn for machine learning, and Seaborn for visualization.
D. cuDF for data manipulation, cuML for machine learning, and Plotly for visualization.

Answer: D

NEW QUESTION # 169
You are optimizing a data pipeline for a large-scale machine learning project using NVIDIA RAPIDS and Apache Spark. The pipeline performs many expensive shuffle operations.
Which of the following is the most effective method to reduce shuffle and improve performance using NVIDIA technologies?

A. Use RAPIDS cuDF to perform in-memory data processing on GPU before shuffling to avoid network communication.
B. Store data in HDFS before performing shuffle operations to reduce GPU memory overhead.
C. Implement a custom shuffle partitioning scheme using NVIDIA DALI for more control over data partitioning during shuffle operations.
D. Use RAPIDS cuDF to repartition the data in the GPU memory after each shuffle.

Answer: A

NEW QUESTION # 170
You are working with a dataset containing billions of records stored in a Parquet file. You need to load this dataset efficiently into an NVIDIA-accelerated RAPIDS environment for feature engineering.
Which of the following is the best approach?

A. Use pandas.read_parquet() to load the dataset and then convert it to a cuDF DataFrame
B. Convert the dataset into a CSV format and use cudf.read_csv() to load it into RAPIDS
C. Load the Parquet file directly into a cuDF DataFrame using cudf.read_parquet()
D. Load the Parquet file into Dask and then convert it into a cuDF DataFrame for parallel processing

Answer: C

NEW QUESTION # 171
In a typical MLOps pipeline, which of the following practices are essential to ensuring robust deployment and monitoring of machine learning models in production? (Select two)

A. Continuous integration and continuous deployment (CI/CD) pipelines for model updates.
B. Post-deployment data drift detection to assess model performance degradation.
C. Use of manual intervention for every model update to ensure accuracy.
D. Automated hyperparameter tuning during inference to optimize model performance.

Answer: A,B

NEW QUESTION # 172
You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?

A. Hadoop MapReduce
B. Pandas with multiprocessing
C. Apache Spark with its default CPU-based execution
D. RAPIDS + Dask for distributed GPU-accelerated ETL

Answer: D

NEW QUESTION # 173
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