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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q86-Q91):

NEW QUESTION # 86
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. Use the DaskExecutor from RAPIDS cuML to offload data processing tasks to distributed GPU workers.
B. Convert Dask DataFrames into Pandas DataFrames to leverage GPU acceleration through Pandas' built-in multi-threading support.
C. Use dask.dataframe.read_parquet() to load the dataset and let Dask automatically distribute computations across multiple GPUs.
D. Explicitly initialize a LocalCUDACluster with multiple workers, ensuring each worker is assigned a single GPU.

Answer: D

NEW QUESTION # 87
You need to determine the optimal data processing library for a small dataset of 500,000 records that will be processed on a multi-core CPU machine with no GPU access.
Which of the following libraries would be the most efficient for this task?

A. Dask
B. pandas
C. PyTorch
D. cuDF

Answer: B

NEW QUESTION # 88
Which tools or technologies from NVIDIA are essential for implementing an efficient MLOps pipeline in production environments? (Select two)

A. NVIDIA NGC for storing and sharing machine learning datasets
B. NVIDIA DLA (Deep Learning Accelerator) for model deployment
C. NVIDIA Triton Inference Server for managing deployment and serving models
D. NVIDIA TensorRT for efficient model inference
E. NVIDIA CUDA for model training in cloud environments

Answer: C,D

NEW QUESTION # 89
A data scientist is working with a large dataset containing missing values and outliers. The dataset will be used for training a machine learning model. The scientist decides to preprocess the data using RAPIDS cuDF, an accelerated dataframe library.
Which of the following is the most efficient approach to handle missing values while maintaining data integrity?

A. Convert missing values to a separate categorical class using df.fillna("missing").
B. Use df.fillna(df.mean()) to replace missing values with the column mean.
C. Use df.dropna() to remove all rows with missing values.
D. Replace missing values with zero using df.fillna(0).

Answer: B

NEW QUESTION # 90
You have trained a machine learning model using cuML as part of the Modeling phase in the CRISP- DM framework. Now, you need to assess how well the model performs before moving forward with deployment.
Which of the following steps aligns best with the Evaluation phase of CRISP-DM using NVIDIA technologies?

A. Define the problem statement and collect relevant datasets before training the model.
B. Deploy the model to an edge device using TensorRT for real-time inference.
C. Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().
D. Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.

Answer: C

NEW QUESTION # 91
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