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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q198-Q203):

NEW QUESTION # 198
A retail company needs to design a real-time ETL workflow to process millions of sales records per second from multiple sources. The data needs to be cleaned, aggregated, and stored for real-time analytics.
Which NVIDIA-powered solution is best suited for this workload?

A. Use NumPy to perform array-based operations on sales data before inserting into a database
B. Use standard Apache Spark on CPU clusters and move transformed data to GPU storage
C. Use NVIDIA Spark RAPIDS to accelerate ETL processing within Apache Spark on GPUs
D. Perform ETL with cuGraph to model sales data as a graph before storage

Answer: C

NEW QUESTION # 199
You are working with a social network dataset containing millions of user interactions and need to identify influential users based on their connectivity and interactions.
Which approach using NVIDIA's cuGraph library is the most appropriate for this task?

A. Apply cuGraph's K-Means clustering to group users with similar connectivity patterns.
B. Use cuGraph's PageRank algorithm to rank users based on their importance in the network.
C. Run a breadth-first search (BFS) on the entire graph to find the most influential users.
D. Use cuGraph's DBSCAN clustering to detect communities in the social network.

Answer: B

NEW QUESTION # 200
Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?

A. Increase batch size to improve accuracy
B. Automatically adjust the learning rate based on the model's convergence
C. Visualize GPU memory utilization over time
D. Modify the training dataset during model execution

Answer: C

NEW QUESTION # 201
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. Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies
B. Running the model in a local Python virtual environment and copying dependencies manually
C. Compiling all dependencies into the host machine and using system-wide installations
D. Using Docker without GPU support and relying on CPU fallback when running TensorFlow

Answer: A

NEW QUESTION # 202
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. Convert the dataset to a cuDF DataFrame and use cudf.DataFrame.fillna() to fill missing values.
B. Drop all missing values using df.dropna() in Pandas before using RAPIDS.
C. Ignore missing values since GPU acceleration can handle incomplete data without performance degradation.
D. Use pandas.DataFrame.fillna() on the dataset before converting it to a cuDF DataFrame.

Answer: A

NEW QUESTION # 203
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