Latest NVIDIA NCP-ADS Exam Objectives - NCP-ADS Certificate Exam

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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)
Topic 2: Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. Synthetic data generation with RAPIDS 3. cuDF and pandas data preprocessing
Topic 3: MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Memory and capacity evaluation 3. Performance benchmarking and 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: 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 6: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. CRISP-DM workflow execution 3. Benchmarking GPU workflows

>> Latest NVIDIA NCP-ADS Exam Objectives <<

NCP-ADS Certificate Exam | NCP-ADS Valid Exam Sample

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q233-Q238):

NEW QUESTION # 233
A data scientist is working with an imbalanced dataset in a fraud detection project. The dataset contains 1 million transactions, but only 2% of them are labeled as fraudulent. To improve the performance of the model, the scientist decides to generate synthetic data using NVIDIA RAPIDS cuDF.
Which of the following approaches is the best way to generate synthetic samples while preserving data characteristics?

A. Use cudf.DataFrame.interpolate(method='linear') to create new fraudulent samples by interpolating between existing ones.
B. Apply cuML.SMOTE() to generate synthetic samples based on the minority class distribution.
C. Use cudf.DataFrame.sample(frac=0.5, replace=True) to oversample the minority class.
D. Use cudf.DataFrame.append(cudf.DataFrame.random()) to create new fraudulent transactions.

Answer: B

NEW QUESTION # 234
You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?

A. NVIDIA Nsight Systems for managing dataset storage and retrieval performance.
B. NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.
C. NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.
D. NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.

Answer: D

NEW QUESTION # 235
You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

A. Ensuring the ETL pipeline uses only batch processing for data ingestion.
B. Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.
C. Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.
D. Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
E. Using a single storage location for both raw and transformed data to simplify the workflow.

Answer: B,C

NEW QUESTION # 236
You are using RAPIDS and Dask-cuDF to process a large-scale ETL pipeline. The workflow involves multiple join and groupby operations, which are causing excessive shuffling.
How can you best optimize caching to reduce shuffle overhead?

A. Force every operation to be recomputed from the raw dataset to ensure accurate results.
B. Split the dataset into multiple smaller Pandas DataFrames and store them in memory for quick retrieval.
C. Cache data using Apache Arrow's in-memory format, but process all operations on CPU.
D. Use dask.persist() to store frequently accessed cuDF DataFrames in GPU memory, reducing recomputation and shuffle operations.

Answer: D

NEW QUESTION # 237
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. Python multiprocessing module
B. Apache Spark with RAPIDS Accelerator
C. Pandas
D. SQLite

Answer: B

NEW QUESTION # 238
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