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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q224-Q229):

NEW QUESTION # 224
You are working with a large dataset in RAPIDS cuDF and plan to standardize the numerical features using cuml.preprocessing.StandardScaler(). However, some columns contain missing values.
What is the best approach to handle the missing values before applying standardization?

A. Use cuml.impute.SimpleImputer(strategy='mean') to replace missing values with the column mean.
B. Replace missing values with zero before standardization.
C. Use cudf.DataFrame.fillna(method='ffill') to forward-fill missing values.
D. cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.

Answer: A

NEW QUESTION # 225
A machine learning engineer wants to accelerate the training of a large deep learning model while optimizing GPU utilization.
Which of the following strategies provides the best performance improvement using NVIDIA technologies?

A. Use NVIDIA DALI (Data Loading Library) to preprocess and augment data on the GPU before feeding it to the model.
B. Use CPU-based data augmentation techniques before transferring the data to the GPU for training.
C. Disable mixed-precision training since it reduces numerical accuracy and does not significantly impact performance.
D. Load all training data into GPU memory at once to minimize CPU-GPU data transfer time.

Answer: A

NEW QUESTION # 226
You are working on a data science project that requires augmenting a dataset using synthetic data.
You are utilizing cuDF and NVIDIA RAPIDS to speed up the data generation process.
Which of the following methods is the most effective way to generate synthetic data using cuDF in a RAPIDS workflow?

A. Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.
B. Use cudf.DataFrame.applymap() to create new synthetic features through complex mathematical functions.
C. Use cuDF to manipulate data distributions and generate new data points based on existing features.

Answer: C

NEW QUESTION # 227
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. Store all data as CSV files and perform ETL operations using traditional row-based processing.
B. 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.
C. Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
D. Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.

Answer: B

NEW QUESTION # 228
A data scientist is working on a social network analysis project where they need to find the most influential users in a large-scale graph dataset. The dataset consists of millions of users connected through directed edges.
Which of the following approaches would be the best choice for this task using NVIDIA GPU-accelerated tools?

A. Use cuGraph's bfs() (Breadth-First Search) to find the most influential nodes.
B. Use cuDF's groupby().sum() function to count the number of connections per user.
C. Convert the graph into a pandas DataFrame and apply NetworkX's PageRank implementation.
D. Use cuGraph's pagerank() function to identify influential nodes based on link structure.

Answer: D

NEW QUESTION # 229
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