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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Distributed data processing frameworks (Dask) 2. GPU-accelerated ETL design and implementation 3. Data caching and performance optimization
Machine Learning	15%	- Model Development and Optimization 1. Feature engineering 2. Hyperparameter tuning 3. Memory optimization techniques (mixed precision, batching) 4. Multi-GPU training comparison
MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Model deployment in production environments 3. Performance benchmarking and optimization
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Perform time series analysis and visualization 2. Detect anomalies in time series datasets 3. Use cuGraph for graph analytics
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. CRISP-DM workflow execution 2. Docker and Conda environment management 3. Benchmarking GPU workflows
Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. cuDF and pandas data preprocessing 3. Synthetic data generation with RAPIDS

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q121-Q126):

NEW QUESTION # 121
You have trained a machine learning model and are deploying it into production in a cloud-based environment. You need to ensure the model is continuously monitored for performance, including drift in input data and prediction quality.
Which of the following techniques should you use to effectively monitor the model in production? (Select two)

A. Implement model versioning and rollback procedures to maintain consistency in production models.
B. Restrict access to the model's API to minimize the number of calls and reduce monitoring overhead.
C. Use manual inspection of model predictions periodically to assess accuracy in production.
D. Set up continuous model retraining pipelines triggered by data drift or performance degradation.

Answer: A,D

NEW QUESTION # 122
You are working on a predictive maintenance model for industrial equipment. The dataset includes various sensor readings, categorical metadata, and timestamped events.
Which data type is the best choice for a feature representing the operating status of a machine, which has three possible states: "Idle", "Running", and "Error"?

A. Categorical encoding (one-hot encoding or ordinal encoding).
B. Floating-point representation (e.g., 0.1 for Idle, 0.5 for Running, 0.9 for Error).
C. Text (storing states as raw strings like "Idle", "Running", "Error").
D. Integer (0, 1, 2) to represent each state numerically.

Answer: A

NEW QUESTION # 123
You are processing a multi-terabyte dataset in CuDF and want to optimize query performance and storage efficiency.
Which approach should you follow to ensure that the dataset remains efficiently partitioned and easily accessible?

A. Split the dataset into multiple files based on a logical partitioning key, such as a date column.
B. Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.
C. Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.
D. Store the entire dataset in a single large file to minimize the number of files in the directory.

Answer: A

NEW QUESTION # 124
A data engineer is tasked with processing a 5 TB dataset stored in Apache Parquet format. The dataset consists of user activity logs and needs to be filtered, aggregated, and processed for feature engineering before training an ML model. The engineer is deciding between Dask and Apache Spark.
Which statement best describes a key difference between the two frameworks?

A. Spark cannot utilize GPUs, whereas Dask has built-in GPU acceleration by default.
B. Spark is better suited for structured and semi-structured data, while Dask excels at unstructured data processing.
C. Dask is a more lightweight solution, often preferred for Python-centric workflows, whereas Spark provides broader ecosystem integrations and supports SQL-like operations natively.
D. Dask is optimized for in-memory processing, while Spark requires disk-based storage for computation.

Answer: C

NEW QUESTION # 125
In the CRISP-DM process, which NVIDIA technology is commonly used during the "Data Preparation" phase to handle large datasets efficiently?

A. NVIDIA Deep Learning AI
B. NVIDIA CUDA Toolkit
C. NVIDIA Jetson
D. NVIDIA RAPIDS

Answer: D

NEW QUESTION # 126
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