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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q201-Q206):

NEW QUESTION # 201
You are processing a large dataset using NVIDIA Dask-cuDF to distribute GPU-accelerated computation across multiple nodes. Users report inconsistent execution times, with some jobs taking significantly longer than expected.
Which of the following actions would best help diagnose the performance bottleneck?

A. Switch to using Pandas with Dask to compare execution speed differences.
B. Limit GPU memory usage to force more frequent spilling to disk and observe performance differences.
C. Reduce the number of Dask workers to minimize parallel execution overhead.
D. Use Dask's dashboard and NVTX markers to analyze task execution times and GPU utilization.

Answer: D

NEW QUESTION # 202
You are analyzing a large-scale transportation network using cuGraph and notice that query times are longer than expected when running graph algorithms.
What is the best way to optimize graph processing performance using GPU-accelerated tools?

A. Store the graph in COO (Coordinate List) format instead of CSR (Compressed Sparse Row) format for faster traversal.
B. Use cugraph.filter_unconnected_nodes() to remove unconnected nodes before processing.
C. Use cugraph.to_directed() to convert the graph into a directed format, which improves GPU parallelism.
D. Convert the graph to CSR (Compressed Sparse Row) format before running computations to improve memory efficiency.

Answer: D

NEW QUESTION # 203
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 optimized for in-memory processing, while Spark requires disk-based storage for computation.
B. Spark cannot utilize GPUs, whereas Dask has built-in GPU acceleration by default.
C. Dask is a more lightweight solution, often preferred for Python-centric workflows, whereas Spark provides broader ecosystem integrations and supports SQL-like operations natively.
D. Spark is better suited for structured and semi-structured data, while Dask excels at unstructured data processing.

Answer: C

NEW QUESTION # 204
A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?

A. Prefetching data with the CPU while training on the GPU
B. Disabling data parallelism
C. Using mixed-precision training with Tensor Cores
D. Running training exclusively on CPU

Answer: C

NEW QUESTION # 205
A machine learning engineer is tasked with deploying a real-time image classification model as part of an MLOps pipeline. The model requires low-latency inference and must handle high-throughput requests efficiently.
Which of the following deployment strategies is the most suitable for leveraging GPU acceleration?

A. Using an Apache Spark cluster with distributed CPU-based inference.
B. Running the model inference on a multi-core CPU server with batch processing enabled.
C. Deploying the model using NVIDIA Triton Inference Server with TensorRT optimizations.
D. Using a REST API wrapper to load the model dynamically into memory for each request.

Answer: C

NEW QUESTION # 206
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