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NVIDIA NCP-ADS Exam Syllabus Topics:

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

>> Examinations NCP-ADS Actual Questions <<

NCP-ADS – 100% Free Examinations Actual Questions | Professional NVIDIA-Certified-Professional Accelerated Data Science Review Guide

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q245-Q250):

NEW QUESTION # 245
You are working with a time-series dataset containing network traffic logs. You suspect the presence of anomalies, such as distributed denial-of-service (DDoS) attacks or sudden traffic surges.
Which machine learning approach is best suited for detecting these anomalies?

A. k-Nearest Neighbors (k-NN) for Classification
B. Naive Bayes Classifier
C. Autoencoder-based Anomaly Detection
D. Linear Regression

Answer: C

NEW QUESTION # 246
You are working with structured tabular data in a cloud-based GPU environment.
Your dataset contains the following columns:
Column Name Example Values Data Type Needed
user_id 15432, 98765, 43210 Integer
purchase_amt 12.99, 35.50, 100.75 Floating Point
category 'Books', 'Electronics' Categorical
Which of the following is the most optimal approach to assign data types to these columns to ensure efficient memory usage and computational performance?

A. 1. df['user_id'] = df['user_id'].astype('int64')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
B. 1. df['user_id'] = df['user_id'].astype('int32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float32')
3. df['category'] = df['category'].astype('category')
C. 1. df['user_id'] = df['user_id'].astype('int16')
2. df['purchase_amt'] = df['purchase_amt'].astype('float16')
3. df['category'] = df['category'].astype('string')
D. 1. df['user_id'] = df['user_id'].astype('float32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')

Answer: B

NEW QUESTION # 247
You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

A. df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)
B. df = (df - df.mean()) / df.std()
C. 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)
D. df = (df - df.min()) / (df.max() - df.min())

Answer: C

NEW QUESTION # 248
A data scientist needs to process a dataset containing 10 million records, performing transformations and exploratory data analysis (EDA). The processing needs to be efficient but does not require high- performance multi-GPU execution.
Which of the following libraries provides the best balance between usability and performance?

A. Pandas, as it provides a simple API and works well for datasets that fit within system memory.
B. Spark DataFrame, as it is optimized for distributed processing and scales well even for 10 million records.
C. Dask DataFrame, since it automatically parallelizes computations even when the dataset fits in memory.
D. cuDF, since GPU acceleration will still provide a speedup even for moderately sized datasets.

Answer: A

NEW QUESTION # 249
You are working on an MLOps project where GPU-accelerated workflows are being used for model training. You want to benchmark and optimize these workflows to ensure the best performance.
Which of the following steps should you consider to effectively benchmark and optimize GPU- accelerated workflows? (Select two)

A. Use a dynamic batch size strategy that adjusts the batch size based on available GPU memory to maximize throughput.
B. Use profiling tools to measure the GPU utilization and memory usage during training to identify performance bottlenecks.
C. Increase the batch size and learning rate simultaneously to maximize GPU usage and reduce training time.
D. Optimize data loading by using data augmentation techniques during training to reduce the time spent on I/O operations.

Answer: A,B

NEW QUESTION # 250
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