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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q215-Q220):

NEW QUESTION # 215
You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?

A. Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
B. Store all data as CSV files and perform ETL operations using traditional row-based processing.
C. Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
D. Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage.

Answer: D

NEW QUESTION # 216
A retail company needs to design a real-time ETL workflow to process millions of sales records per second from multiple sources. The data needs to be cleaned, aggregated, and stored for real-time analytics.
Which NVIDIA-powered solution is best suited for this workload?

A. Use standard Apache Spark on CPU clusters and move transformed data to GPU storage
B. Perform ETL with cuGraph to model sales data as a graph before storage
C. Use NumPy to perform array-based operations on sales data before inserting into a database
D. Use NVIDIA Spark RAPIDS to accelerate ETL processing within Apache Spark on GPUs

Answer: D

NEW QUESTION # 217
You are using RAPIDS and Dask-cuDF to process a large-scale ETL pipeline. The workflow involves multiple join and groupby operations, which are causing excessive shuffling.
How can you best optimize caching to reduce shuffle overhead?

A. Cache data using Apache Arrow's in-memory format, but process all operations on CPU.
B. Use dask.persist() to store frequently accessed cuDF DataFrames in GPU memory, reducing recomputation and shuffle operations.
C. Force every operation to be recomputed from the raw dataset to ensure accurate results.
D. Split the dataset into multiple smaller Pandas DataFrames and store them in memory for quick retrieval.

Answer: B

NEW QUESTION # 218
A machine learning team is handling large-scale datasets that need to be efficiently stored and accessed within an NVIDIA RAPIDS workflow.
Which of the following storage formats and techniques provides the best performance for GPU-based data science pipelines?

A. Store data in SQLite databases and query it into pandas before converting it to cuDF.
B. Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.
C. Store data in the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.
D. Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.

Answer: C

NEW QUESTION # 219
In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A. Efficient handling of matrix operations in machine learning models
B. Lower cost of cloud infrastructure
C. Lower energy consumption compared to CPUs
D. Faster parallel processing for large datasets
E. Better for memory-intensive workloads

Answer: A,D

NEW QUESTION # 220
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