NCP-ADS Actual Dumps - NCP-ADS Valid Exam Pattern

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q154-Q159):

NEW QUESTION # 154
A machine learning engineer is tasked with deploying a real-time image classification model as part of an MLOps pipeline. The model requires low-latency inference and must handle high-throughput requests efficiently.
Which of the following deployment strategies is the most suitable for leveraging GPU acceleration?

A. Running the model inference on a multi-core CPU server with batch processing enabled.
B. Using an Apache Spark cluster with distributed CPU-based inference.
C. Using a REST API wrapper to load the model dynamically into memory for each request.
D. Deploying the model using NVIDIA Triton Inference Server with TensorRT optimizations.

Answer: D

NEW QUESTION # 155
A team of data engineers is working on an Apache Spark-based distributed computing pipeline that leverages NVIDIA GPUs and RAPIDS. They notice that shuffle operations are causing significant slowdowns in performance.
Which optimization strategy should they implement to reduce shuffle impact?

A. Use RAPIDS Spark-RAPIDS Plugin with GPU-accelerated caching to minimize redundant shuffle operations.
B. Disable GPU memory caching to allow automatic CPU-based shuffle optimization.
C. Store shuffle data in Apache Parquet format on disk for faster access and reduced memory overhead.
D. Use Spark's default shuffle partitioning without any modification, as GPUs inherently optimize shuffle operations.

Answer: A

NEW QUESTION # 156
Which of the following is the best approach for performing benchmarking and optimizing GPU- accelerated workflows for MLOps using Nvidia technologies?

A. Use Nvidia's nsight tools to benchmark only the model training phase and ignore the inference phase, as training is the primary bottleneck.
B. Rely exclusively on the nvidia-smi tool for monitoring GPU utilization and memory usage across multiple GPUs without making any other performance adjustments.
C. Use Nvidia's nvprof tool to profile GPU resource usage and identify bottlenecks, then adjust the batch size to optimize throughput.
D. Use TensorRT to optimize deep learning models by converting them into highly optimized inference engines, allowing faster execution with lower latency.

Answer: C

NEW QUESTION # 157
You are working on a large-scale data processing pipeline that involves multi-GPU acceleration using Dask. The dataset is too large to fit into the memory of a single GPU, so you decide to distribute the workload across multiple GPUs using Dask-CUDA.
Which of the following steps is necessary to implement efficient data parallelism across multiple GPUs in a Dask-based workflow?

A. Explicitly initialize a LocalCUDACluster with multiple workers, ensuring each worker is assigned a single GPU.
B. Use dask.dataframe.read_parquet() to load the dataset and let Dask automatically distribute computations across multiple GPUs.
C. Use the DaskExecutor from RAPIDS cuML to offload data processing tasks to distributed GPU workers.
D. Convert Dask DataFrames into Pandas DataFrames to leverage GPU acceleration through Pandas' built-in multi-threading support.

Answer: A

NEW QUESTION # 158
A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually configuring each system.
Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?

A. Using Docker without GPU support and relying on CPU fallback when running TensorFlow
B. Running the model in a local Python virtual environment and copying dependencies manually
C. Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies
D. Compiling all dependencies into the host machine and using system-wide installations

Answer: C

NEW QUESTION # 159
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