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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
MLOps	19%	- End-to-end workflow management - Pipeline automation and orchestration - Model deployment and serving - Monitoring, logging and maintenance
Data Manipulation and Software Literacy	19%	- Dependency management and containerization - GPU-accelerated ETL workflows - Performance profiling and optimization tools - Data processing libraries selection and usage
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
Data Analysis	14%	- Time-series analysis and anomaly detection - Distributed and parallel data processing - Data visualization and graph analytics - Exploratory Data Analysis (EDA)
Machine Learning	15%	- Model evaluation and validation - Model training and hyperparameter tuning - Distributed training strategies - GPU-accelerated ML frameworks and algorithms
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 (Q138-Q143):

NEW QUESTION # 138
A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually configuring each system.
Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?

A. Compiling all dependencies into the host machine and using system-wide installations
B. Running the model in a local Python virtual environment and copying dependencies manually
C. Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies
D. Using Docker without GPU support and relying on CPU fallback when running TensorFlow

Answer: C

NEW QUESTION # 139
You are tasked with implementing a multi-GPU data pipeline using Dask-CUDA to process large datasets stored in Parquet format. Your goal is to achieve optimal GPU memory utilization and minimize inter-GPU communication overhead.
Which of the following approaches best aligns with these goals?

A. Use dask.persist() instead of dask.compute() to force immediate execution of tasks before distribution to GPUs.
B. Use dask_cudf.read_parquet() with split_row_groups=True to evenly distribute data across GPUs.
C. Set dask.config.set({'distributed.worker.memory.target': 0.9}) to allocate 90% of the total CPU memory for GPU operations.
D. Use dask.array instead of dask_cudf because it provides better performance for structured tabular data.

Answer: B

NEW QUESTION # 140
You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

A. Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations
B. Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold
C. Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence
D. Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format

Answer: B

NEW QUESTION # 141
A data scientist is analyzing a large time-series dataset containing stock price movements of thousands of companies over a decade. The dataset is stored as a cuDF DataFrame and contains millions of rows. The scientist wants to visualize trends and patterns interactively while leveraging GPU acceleration.
Which of the following approaches is the most efficient for visualizing this time-series data?

A. Convert the cuDF DataFrame to Pandas and use matplotlib for plotting.
B. Use cuXfilter with a cuDF DataFrame to generate interactive visualizations directly on the GPU.
C. Use seaborn with a sampled subset of the dataset to generate line plots.
D. Use matplotlib with plt.plot() while applying df.to_pandas() to convert data.

Answer: B

NEW QUESTION # 142
You are a data scientist working on a large-scale deep learning project that requires significant computational resources. You have the option to run your workloads on a cloud-based GPU instance.
Which of the following statements best describes a key benefit of using cloud-based GPUs for your workload?

A. Cloud-based GPUs eliminate all data transfer bottlenecks and latencies when training models on large datasets.
B. Cloud-based GPUs provide consistent and predictable performance, identical to on-premise dedicated GPUs.
C. Cloud-based GPUs enable scalable resource allocation, allowing you to dynamically increase or decrease GPU instances as needed.
D. Cloud-based GPUs are always more cost-effective than on-premise GPUs, regardless of workload size and duration.

Answer: C

NEW QUESTION # 143
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