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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q247-Q252):

NEW QUESTION # 247
You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?

A. Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
B. Store all data as CSV files and perform ETL operations using traditional row-based processing.
C. Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
D. Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage.

Answer: D

NEW QUESTION # 248
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. Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.
B. Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.
C. Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.
D. Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.

Answer: D

NEW QUESTION # 249
You are analyzing a transportation network where airports represent nodes and flight routes represent edges. You need to determine the most critical airports in the network based on how many shortest paths pass through them.
Which cuGraph centrality algorithm should you use for this task?

A. Degree Centrality, as it measures the number of direct connections an airport has, indicating its importance in the network.
B. Triangle Count, as it determines the number of three-node cycles an airport is part of, indicating its importance in network structure.
C. Closeness Centrality, as it measures how close an airport is to all other airports, making it the best indicator of global connectivity.
D. Betweenness Centrality, as it identifies airports that act as major transit hubs by measuring how frequently they appear in shortest paths.

Answer: D

NEW QUESTION # 250
You are preprocessing a dataset using NVIDIA RAPIDS cuDF and need to handle missing values in the column temperature by replacing them with the column's median value.
Which of the following approaches correctly achieves this in an optimized manner?

A. df['temperature'].fillna(df['temperature'].median(), inplace=True)
B. df['temperature'].dropna(inplace=True)
C. df['temperature'].fillna(df['temperature'].mean(), inplace=True)
D. 1. df['temperature'] = df['temperature'].map(2. lambda x: df['temperature'].median() if x is None else x
3.)

Answer: A

NEW QUESTION # 251
You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.
Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

A. df.info(memory_usage='deep')
B. df.memory_usage(deep=True).sum()
C. np.array(df).nbytes
D. sys.getsizeof(df)

Answer: B

NEW QUESTION # 252
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