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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q107-Q112):

NEW QUESTION # 107
A data scientist is deploying a deep learning model to production using an NVIDIA GPU-powered inference server. They want to ensure low latency and high throughput while maintaining model accuracy.
Which of the following deployment strategies would be most effective?

A. Running inference on a CPU instead of a GPU to reduce power consumption
B. Using NVIDIA Triton Inference Server with model ensemble optimization
C. Deploying a model without batching to ensure real-time responses
D. Disabling mixed-precision inference to avoid accuracy loss

Answer: B

NEW QUESTION # 108
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. Dask DataFrame, since it automatically parallelizes computations even when the dataset fits in memory.
C. cuDF, since GPU acceleration will still provide a speedup even for moderately sized datasets.
D. Spark DataFrame, as it is optimized for distributed processing and scales well even for 10 million records.

Answer: A

NEW QUESTION # 109
You are training a machine learning model using RAPIDS cuML and need to ensure that all numeric features are standardized for better model performance.
Which of the following is the best approach for scaling data using RAPIDS?

A. df_scaled = (df - df.min()) / (df.max() - df.min())
B. df_scaled = df / df.max()
C. df_scaled = scaler.fit_transform(df)
D. scaler = cuml.preprocessing.StandardScaler()
E. df_scaled = df.apply(lambda x: x / np.linalg.norm(x))

Answer: D

NEW QUESTION # 110
You are building a real-time recommendation system that processes high-frequency transactional data from millions of users.
The system must:
- Ingest and preprocess data efficiently
- Perform similarity computations for user-item recommendations
- Scale to handle rapid incoming transactions
Which of the following NVIDIA technologies is the best choice for this use case?

A. NVIDIA NVTabular
B. RAPIDS cuGraph
C. NVIDIA Triton Inference Server
D. CUDA Kernels with Custom C++ Code

Answer: A

NEW QUESTION # 111
You need to deploy a containerized machine learning model that utilizes NVIDIA GPUs on a cloud- based Kubernetes cluster.
Which of the following steps is essential for ensuring proper GPU utilization inside a Docker container?

A. Run the container using the default Docker runtime without any additional configurations
B. Install GPU drivers inside the container to ensure access to the host's hardware
C. Use the NVIDIA Container Toolkit (nvidia-docker2) to allow GPU access within Docker containers
D. Use a standard Python-based container image instead of an NVIDIA GPU-optimized image

Answer: C

NEW QUESTION # 112
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