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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q121-Q126):

NEW QUESTION # 121
You are tasked with profiling a PyTorch-based deep learning model to identify performance bottlenecks using NVIDIA DLProf. Your goal is to analyze kernel execution times and identify operations causing excessive memory consumption.
Which of the following steps is the MOST appropriate sequence for profiling using DLProf?

A. Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.
B. Execute the training script under DLProf TensorBoard mode to visualize performance insights, then re-run the model with automatic mixed precision (AMP) to reduce memory usage.
C. Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.
D. Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.

Answer: D

NEW QUESTION # 122
A research team is analyzing large-scale social interactions and wants to identify strongly connected communities within a massive graph dataset using NVIDIA's cuGraph library.
Which method would be the most efficient for this task?

A. Use cuGraph's Louvain method to detect hierarchical communities based on modularity optimization.
B. Run the cuGraph PageRank algorithm and classify nodes with high scores as community leaders.
C. Apply cuGraph's Label Propagation Algorithm (LPA) to divide the graph into communities without predefining the number of clusters.
D. Apply cuGraph's Dijkstra's algorithm to find the shortest paths between all nodes and group them into communities.

Answer: A

NEW QUESTION # 123
A data scientist is training a deep learning model on an NVIDIA GPU and wants to profile the model to identify performance bottlenecks. The scientist chooses to use NVIDIA DLProf.
Which of the following steps is the most effective way to profile the model using DLProf?

A. Run the model using dlprof --mode profile to collect performance metrics and generate a report.
B. Use nvprof instead of DLProf since it provides more detailed profiling for deep learning workloads.
C. Modify the training script to manually insert timing functions for each layer and compare execution times.
D. Rely on general CPU profiling tools like perf and gprof to analyze GPU performance.

Answer: A

NEW QUESTION # 124
You are working with a dataset consisting of 100 million records stored in a distributed system. The dataset includes numerical and categorical variables, requiring both exploratory data analysis (EDA) and machine learning model training. The processing time using traditional CPU-based methods is too slow.
Which of the following techniques would be the most effective acceleration method to handle this workload efficiently?

A. Reduce the dataset to a smaller sample size before processin
B. Use RAPIDS cuDF for GPU-accelerated data processing
C. Scale up to a high-core-count CPU machine
D. Store the dataset in a relational database and query it using SQL

Answer: B

NEW QUESTION # 125
Which of the following is the most efficient method for processing big data in a distributed environment using NVIDIA technologies?

A. Using a single-node GPU-based solution for data processing.
B. Relying on traditional CPU-only processing frameworks for big data tasks.
C. Using cloud-based, non-GPU infrastructure for data processing
D. Utilizing a distributed data processing framework like Apache Spark with GPU acceleration from the NVIDIA RAPIDS library.

Answer: D

NEW QUESTION # 126
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