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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q272-Q277):

NEW QUESTION # 272
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 NumPy to perform array-based operations on sales data before inserting into a database
B. Use standard Apache Spark on CPU clusters and move transformed data to GPU storage
C. Use NVIDIA Spark RAPIDS to accelerate ETL processing within Apache Spark on GPUs
D. Perform ETL with cuGraph to model sales data as a graph before storage

Answer: C

NEW QUESTION # 273
You are working on a machine learning dataset with millions of rows and want to optimize performance on a GPU using CuDF. One of the features in your dataset represents categorical data with a limited number of unique values.
Which of the following data types should you use to maximize performance while reducing memory usage?

A. category
B. int64
C. bool
D. object (string)

Answer: A

NEW QUESTION # 274
You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?

A. Perform all EDA using NumPy and SciPy for optimized array computations.
B. Use Pandas directly for data manipulation and visualization.
C. Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.
D. Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU.

Answer: D

NEW QUESTION # 275
Which tools or technologies from NVIDIA are essential for implementing an efficient MLOps pipeline in production environments? (Select two)

A. NVIDIA Triton Inference Server for managing deployment and serving models
B. NVIDIA NGC for storing and sharing machine learning datasets
C. NVIDIA CUDA for model training in cloud environments
D. NVIDIA TensorRT for efficient model inference
E. NVIDIA DLA (Deep Learning Accelerator) for model deployment

Answer: A,D

NEW QUESTION # 276
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 repartition the data in the GPU memory after each shuffle.
B. Use RAPIDS cuDF to perform in-memory data processing on GPU before shuffling to avoid network communication.
C. Store data in HDFS before performing shuffle operations to reduce GPU memory overhead.
D. Implement a custom shuffle partitioning scheme using NVIDIA DALI for more control over data partitioning during shuffle operations.

Answer: B

NEW QUESTION # 277
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