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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Distributed and parallel data processing - Time-series analysis and anomaly detection - Data visualization and graph analytics - Exploratory Data Analysis (EDA)
Topic 2: GPU and Cloud Computing	16%	- CRISP-DM and data science methodology - Cloud GPU environments and deployment - GPU architecture and acceleration principles - Resource management and scaling strategies
Topic 3: Data Manipulation and Software Literacy	19%	- GPU-accelerated ETL workflows - Performance profiling and optimization tools - Data processing libraries selection and usage - Dependency management and containerization
Topic 4: MLOps	19%	- Pipeline automation and orchestration - Model deployment and serving - End-to-end workflow management - Monitoring, logging and maintenance
Topic 5: Data Preparation	17%	- Workflow monitoring and bottleneck identification - Data validation and quality assurance - Feature engineering and data type optimization - Data cleaning, preprocessing and transformation
Topic 6: Machine Learning	15%	- Model evaluation and validation - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning - Distributed training strategies

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q17-Q22):

NEW QUESTION # 17
You are analyzing a dataset containing millions of transaction records to detect fraudulent transactions. The dataset is highly imbalanced, with fraudulent cases making up only 0.5% of the data.
Which data analysis strategy will most effectively handle the imbalance while minimizing the risk of overfitting?

A. SMOTE (Synthetic Minority Over-sampling Technique)
B. Stratified random sampling
C. Random undersampling of the majority class
D. Random oversampling of the minority class

Answer: C

NEW QUESTION # 18
You are working on a machine learning project that requires selecting the optimal data types for each feature in your dataset to maximize performance and efficiency in an MLOps pipeline.
Which of the following data types is most suitable for GPU-accelerated machine learning workflows when working with large datasets on NVIDIA platforms?

A. Int32
B. Float64
C. String
D. Float32

Answer: D

NEW QUESTION # 19
A financial services company is using NVIDIA RAPIDS cuML to train a credit risk assessment model.
The dataset contains hundreds of numerical and categorical features, including loan amount, credit score, income, employment history, and previous loan defaults.
To optimize feature selection using NVIDIA technologies, which approach should they take?

A. Convert all numerical features into categorical variables to simplify the data and reduce the need for feature selection.
B. Avoid feature selection, as modern deep learning models are capable of automatically handling redundant and irrelevant features.
C. Keep all features in the dataset to ensure the model captures as much information as possible, even if some features are redundant.
D. Use cuML's feature selection algorithms, such as Recursive Feature Elimination (RFE), to identify the most important predictors.

Answer: D

NEW QUESTION # 20
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. Using Apache Arrow to optimize in-memory columnar processing within Spark
C. Switching to Dask and RAPIDS to fully utilize GPU acceleration
D. Rewriting the entire ETL pipeline in CUDA to maximize GPU efficiency

Answer: A

NEW QUESTION # 21
You are optimizing a data pipeline for a large-scale machine learning project using NVIDIA RAPIDS and Apache Spark. The pipeline performs many expensive shuffle operations.
Which of the following is the most effective method to reduce shuffle and improve performance using NVIDIA technologies?

A. Use RAPIDS cuDF to perform in-memory data processing on GPU before shuffling to avoid network communication.
B. Store data in HDFS before performing shuffle operations to reduce GPU memory overhead.
C. Use RAPIDS cuDF to repartition the data in the GPU memory after each shuffle.
D. Implement a custom shuffle partitioning scheme using NVIDIA DALI for more control over data partitioning during shuffle operations.

Answer: A

NEW QUESTION # 22
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