NCP-ADS Exam Topics, Fresh NCP-ADS Dumps

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

>> NCP-ADS Exam Topics <<

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q14-Q19):

NEW QUESTION # 14
When scaling a distributed data processing framework using NVIDIA GPU technology for big data processing, which of the following factors is most critical to optimize performance?

A. Maximizing the amount of data transferred between GPUs for faster processing.
B. Ensuring the proper configuration of GPU resources across all nodes in the distributed system.
C. Limiting the number of GPU nodes used in the cluster to avoid complexity.
D. Using more CPU cores to handle computation-heavy tasks.

Answer: B

NEW QUESTION # 15
You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
Which of the following is the most memory-efficient data type choice for this column?

A. df['item_id'] = df['item_id'].astype('float64')
B. df['item_id'] = df['item_id'].astype('int64')
C. df['item_id'] = df['item_id'].astype('int32')
D. df['item_id'] = df['item_id'].astype('string')

Answer: B

NEW QUESTION # 16
You are tasked with acquiring a dataset for training a machine learning model in healthcare, predicting patient readmission rates. Before using the dataset, you must assess its quality.
Which of the following is the most important factor to evaluate before acquisition?

A. The programming language used to preprocess the dataset
B. The number of missing values and inconsistencies in key columns
C. The file format (CSV, JSON, or XML) of the dataset
D. Whether the dataset is stored on a cloud server or a local machine

Answer: B

NEW QUESTION # 17
You have a pandas DataFrame with a column containing floating-point numbers, but it takes up too much memory. You want to convert it into a lower-precision type using CuDF or pandas while ensuring computational efficiency.
Which function would you use?

A. df.convert_dtypes()
B. df.to_float16()
C. df.astype('float16')
D. df['col'].apply(lambda x: np.float16(x))

Answer: C

NEW QUESTION # 18
In Python, when working with large datasets using pandas, which of the following methods are best for improving performance and efficiency when applying operations on DataFrames? (Select two)

A. Using apply() function over DataFrame rows
B. Using map() function to apply a function element-wise
C. Using for loops to apply operations row by row
D. Using vectorized operations (e.g., element-wise arithmetic)
E. Using iterrows() for iterating through DataFrame rows

Answer: B,D

NEW QUESTION # 19
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