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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q96-Q101):

NEW QUESTION # 96
You are working with a data science project that requires GPU acceleration for machine learning tasks. Your team is facing challenges with software version conflicts between different dependencies when deploying the project on different systems.
Which of the following solutions should you consider to efficiently manage software dependencies and avoid conflicts? (Select two)

A. Use Docker to containerize the project, ensuring that the dependencies and environment are consistent across different systems.
B. Set up a virtual machine for each different dependency configuration to isolate environments.
C. Install GPU drivers on the host machine and rely on the local system environment for dependency management.
D. Manually install all dependencies directly on the host machine to avoid using dependency management tools.
E. Use Conda to create isolated environments for different versions of dependencies, ensuring version compatibility.

Answer: A,E

NEW QUESTION # 97
You are working on a large-scale machine learning pipeline that involves processing massive datasets using multiple GPUs on an NVIDIA DGX system. You choose to use Dask to enable efficient parallel processing across multiple GPUs.
Which of the following steps is essential to correctly configure Dask for multi-GPU acceleration?

A. Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.
B. Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.
C. Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.
D. Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.

Answer: C

NEW QUESTION # 98
You are comparing the performance of GPU-accelerated deep learning models on two cloud platforms: AWS EC2 and Google Cloud Platform (GCP). You want to design a benchmark that evaluates GPU resource utilization, processing time, and cost-efficiency for training models with large datasets.
Which actions should you take to implement an effective benchmark? (Select two)

A. Run each model on the same GPU type and instance size across both cloud platforms.
B. Only consider the cost of GPU resources for each cloud provider without factoring in data storage and transfer costs.
C. Limit the benchmarking tests to a single model architecture for each cloud platform.
D. Measure both GPU utilization and network latency between the cloud platform and your data storage location.

Answer: A,D

NEW QUESTION # 99
A data scientist is working with an imbalanced dataset in a fraud detection project. The dataset contains 1 million transactions, but only 2% of them are labeled as fraudulent. To improve the performance of the model, the scientist decides to generate synthetic data using NVIDIA RAPIDS cuDF.
Which of the following approaches is the best way to generate synthetic samples while preserving data characteristics?

A. Use cudf.DataFrame.sample(frac=0.5, replace=True) to oversample the minority class.
B. Use cudf.DataFrame.append(cudf.DataFrame.random()) to create new fraudulent transactions.
C. Apply cuML.SMOTE() to generate synthetic samples based on the minority class distribution.
D. Use cudf.DataFrame.interpolate(method='linear') to create new fraudulent samples by interpolating between existing ones.

Answer: C

NEW QUESTION # 100
You are tasked with optimizing an ETL pipeline that processes petabytes of data daily. Your organization is already using Apache Spark for distributed data processing but is experiencing performance bottlenecks. You need a solution that improves execution speed without requiring extensive code modifications.
Which of the following solutions best meets your needs?

A. Using the NVIDIA Spark RAPIDS Accelerator to run Spark workloads on GPUs
B. Switching to Dask and RAPIDS to fully utilize GPU acceleration
C. Using Apache Arrow to optimize in-memory columnar processing within Spark
D. Rewriting the entire ETL pipeline in CUDA to maximize GPU efficiency

Answer: A

NEW QUESTION # 101
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