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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 - GPU-accelerated ML frameworks and algorithms - Model evaluation and validation
Topic 3: MLOps	19%	- Pipeline automation and orchestration - End-to-end workflow management - Model deployment and serving - Monitoring, logging and maintenance
Topic 4: Data Preparation	17%	- Workflow monitoring and bottleneck identification - Feature engineering and data type optimization - Data validation and quality assurance - Data cleaning, preprocessing and transformation
Topic 5: Data Manipulation and Software Literacy	19%	- Data processing libraries selection and usage - GPU-accelerated ETL workflows - Dependency management and containerization - Performance profiling and optimization tools
Topic 6: GPU and Cloud Computing	16%	- Resource management and scaling strategies - GPU architecture and acceleration principles - CRISP-DM and data science methodology - Cloud GPU environments and deployment

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q160-Q165):

NEW QUESTION # 160
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 Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
B. Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
C. 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.
D. Store all data as CSV files and perform ETL operations using traditional row-based processing.

Answer: C

NEW QUESTION # 161
A data engineer is tasked with processing a 5 TB dataset stored in Apache Parquet format. The dataset consists of user activity logs and needs to be filtered, aggregated, and processed for feature engineering before training an ML model. The engineer is deciding between Dask and Apache Spark.
Which statement best describes a key difference between the two frameworks?

A. Dask is a more lightweight solution, often preferred for Python-centric workflows, whereas Spark provides broader ecosystem integrations and supports SQL-like operations natively.
B. Dask is optimized for in-memory processing, while Spark requires disk-based storage for computation.
C. Spark is better suited for structured and semi-structured data, while Dask excels at unstructured data processing.
D. Spark cannot utilize GPUs, whereas Dask has built-in GPU acceleration by default.

Answer: A

NEW QUESTION # 162
A data scientist is working with a 50 TB dataset consisting of structured logs from IoT devices. The data needs to be cleaned, transformed, and aggregated before training a machine learning model.
Which of the following frameworks would be the most efficient choice for distributed data processing?

A. Python multiprocessing module
B. Pandas
C. SQLite
D. Apache Spark with RAPIDS Accelerator

Answer: D

NEW QUESTION # 163
A data scientist wants to compare the performance of two different GPU-accelerated data science frameworks, NVIDIA RAPIDS (cuDF, cuML) and TensorFlow, for a tabular data classification task.
Which of the following approaches would be the best practice for designing an unbiased and effective benchmark?

A. Use TensorFlow's built-in training time metrics without comparing equivalent RAPIDS-based operations.
B. Run all benchmarks on a CPU to ensure fairness across frameworks.
C. Ignore preprocessing and focus only on model training speed when comparing performance.
D. Measure execution time and memory usage for each framework using NVIDIA Nsight Systems (nsys).

Answer: D

NEW QUESTION # 164
Which of the following best describes the purpose of the NVIDIA TensorRT library?

A. Accelerates training of neural networks
B. Manages GPU resources for deep learning models
C. Optimizes and accelerates inference of trained models
D. Provides hardware abstraction for AI model development

Answer: C

NEW QUESTION # 165
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