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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q249-Q254):

NEW QUESTION # 249
A data scientist is processing a dataset that is too large to fit into the memory of a single GPU. They decide to use Dask with cuDF to leverage multiple GPUs for accelerated computation.
Which of the following approaches ensures efficient parallelism when working with dask_cudf?

A. Use a single-threaded scheduler to avoid contention when running on multiple GPUs.
B. Assign explicit chunks of data to each worker by manually dividing the dataset before passing it to Dask.
C. Use Dask futures to asynchronously submit GPU tasks and collect results when completed.
D. Convert the dataset into a Dask cuDF dataframe and use persist() to keep it in GPU memory.

Answer: D

NEW QUESTION # 250
A research team is analyzing a large transportation network and wants to identify groups of locations that are closely connected based on travel routes.
Which NVIDIA cuGraph function would be the most effective for this task?

A. Use cuGraph's pagerank() to rank the most connected locations.
B. Use cuGraph's louvain() function to detect communities within the graph.
C. Apply cuGraph's bfs() (Breadth-First Search) to find clusters of locations.
D. Use cuGraph's shortest_path() to compute all shortest paths and derive clusters manually.

Answer: B

NEW QUESTION # 251
You are using RAPIDS cuML to train a regression model on a dataset with features of varying scales (temperature in Celsius, revenue in thousands, customer age). To improve model performance, you decide to standardize the data.
Which approach correctly standardizes the data using NVIDIA technologies?

A. Use cuml.StandardScaler() to transform the features to have a mean of zero and a standard deviation of one.
B. Use cuml.PCA() to reduce the dimensionality of the dataset, which also standardizes feature variance.
C. Use cuml.MinMaxScaler() to scale the features to a range of [0,1] without adjusting for mean and variance.
D. Use numpy.mean() and numpy.std() to manually standardize the dataset before feeding it into the GPU.

Answer: A

NEW QUESTION # 252
You are tasked with designing a benchmark to compare the performance of different GPU- accelerated machine learning frameworks, such as TensorFlow, PyTorch, and RAPIDS.
Which of the following factors is the most critical to ensure a fair and meaningful comparison?

A. Using different model architectures per framework to test a variety of scenarios and ensure a broad evaluation.
B. Ensuring all frameworks use the same dataset, batch size, and GPU hardware during benchmarking.
C. Allowing each framework to run with its default settings, as tuning hyperparameters would bias the results.
D. Using the latest software versions of each framework, regardless of compatibility with the GPU hardware.

Answer: B

NEW QUESTION # 253
You are working with a large dataset containing missing values, and you need to clean and preprocess the data efficiently.
Which of the following methods provides the best performance when handling missing values in a GPU- accelerated EDA workflow using RAPIDS?

A. Drop all missing values using df.dropna() in Pandas before using RAPIDS.
B. Use pandas.DataFrame.fillna() on the dataset before converting it to a cuDF DataFrame.
C. Convert the dataset to a cuDF DataFrame and use cudf.DataFrame.fillna() to fill missing values.
D. Ignore missing values since GPU acceleration can handle incomplete data without performance degradation.

Answer: C

NEW QUESTION # 254
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