NCP-ADS Test Lab Questions | Exam NCP-ADS Simulator Free

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning	15%	- Model Development and Optimization 1. Memory optimization techniques (mixed precision, batching) 2. Hyperparameter tuning 3. Feature engineering 4. Multi-GPU training comparison
Topic 2: MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Performance benchmarking and optimization 3. Memory and capacity evaluation
Topic 3: Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. Data normalization and standardization 3. cuDF and pandas data preprocessing
Topic 4: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. Distributed data processing frameworks (Dask) 3. GPU-accelerated ETL design and implementation
Topic 5: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Detect anomalies in time series datasets 2. Use cuGraph for graph analytics 3. Perform time series analysis and visualization
Topic 6: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. Benchmarking GPU workflows 3. CRISP-DM workflow execution

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q152-Q157):

NEW QUESTION # 152
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. Use a standard Python-based container image instead of an NVIDIA GPU-optimized image
C. Use the NVIDIA Container Toolkit (nvidia-docker2) to allow GPU access within Docker containers
D. Install GPU drivers inside the container to ensure access to the host's hardware

Answer: C

NEW QUESTION # 153
You are tasked with profiling a deep learning model using NVIDIA's DLProf to identify performance bottlenecks and optimize resource utilization.
Which of the following statements correctly describes the capabilities of DLProf?

A. DLProf only works with TensorFlow models and does not support PyTorch-based workloads.
B. DLProf can generate detailed reports that highlight kernel-level execution times and GPU utilization trends.
C. DLProf requires significant modifications to the source code to collect profiling data.
D. DLProf is primarily designed for debugging model accuracy rather than performance analysis.

Answer: B

NEW QUESTION # 154
A data scientist is working with a 50 TB dataset consisting of structured logs from IoT devices. The data needs to be cleaned, transformed, and aggregated before training a machine learning model.
Which of the following frameworks would be the most efficient choice for distributed data processing?

A. Pandas
B. Apache Spark with RAPIDS Accelerator
C. SQLite
D. Python multiprocessing module

Answer: B

NEW QUESTION # 155
You are working on an AI-driven customer behavior prediction project.
According to the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, what is the most critical task to complete during the Data Understanding phase?

A. Acquiring and exploring the dataset to assess quality, completeness, and potential biases.
B. Selecting the most appropriate machine learning algorithm for the prediction task.
C. Identifying and preparing feature engineering strategies to improve model accuracy.
D. Deploying the model into a production environment for real-time inference.

Answer: A

NEW QUESTION # 156
You are working on a data science project where you need to process a large dataset containing
500 million records. You want to determine whether GPU acceleration would significantly improve performance.
Which of the following factors best indicates that you should use an accelerated computing solution like RAPIDS?

A. The dataset is heavily structured but mainly requires text-based analysis using regex-based search and manipulation.
B. The dataset consists of simple arithmetic operations on a few columns and can be processed using vectorized NumPy operations.
C. The dataset is a structured table with less than 100,000 records and can be handled efficiently with a Pandas DataFrame.
D. The dataset has high-dimensional sparse features and requires complex operations such as nearest neighbor search and clustering.

Answer: D

NEW QUESTION # 157
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