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| Section | Weight | Objectives |
|---|
| Topic 1: 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 2: 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 3: MLOps | 19% | - Deployment and Monitoring
- 1. Memory and capacity evaluation
- 2. Performance benchmarking and optimization
- 3. Model deployment in production environments
|
| Topic 4: GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
- 1. Benchmarking GPU workflows
- 2. Docker and Conda environment management
- 3. CRISP-DM workflow execution
|
| Topic 5: 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
|
| Topic 6: Machine Learning | 15% | - Model Development and Optimization
- 1. Hyperparameter tuning
- 2. Feature engineering
- 3. Memory optimization techniques (mixed precision, batching)
- 4. Multi-GPU training comparison
|
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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q136-Q141):
NEW QUESTION # 136
You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?
- A. Store all data as CSV files and perform ETL operations using traditional row-based processing.
- B. Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage.
- C. Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
- D. Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
Answer: B
NEW QUESTION # 137
When comparing the required memory with the available memory on a GPU for an MLOps deployment using NVIDIA technologies, which of the following is the best method to optimize memory usage while training large models?
- A. Increase the input data size to fully utilize available memory and improve training performance.
- B. Use mixed-precision training to reduce memory requirements by using half-precision floating-point numbers.
- C. Decrease the number of training iterations to reduce memory consumption.
- D. Use a higher number of GPUs to distribute the model and memory load across the GPUs.
Answer: B
NEW QUESTION # 138
You are analyzing a time-series dataset that represents temperature readings from an industrial sensor. Your goal is to detect anomalies that may indicate sensor failures or environmental changes.
Which of the following methods would be the most appropriate statistical technique for detecting anomalies in this time-series dataset?
- A. Moving Average Smoothing
- B. Z-score (Standard Score) Analysis
- C. One-Hot Encoding
- D. Principal Component Analysis (PCA)
Answer: B
NEW QUESTION # 139
A data engineer is tasked with processing a 5 TB dataset stored in Apache Parquet format. The dataset consists of user activity logs and needs to be filtered, aggregated, and processed for feature engineering before training an ML model. The engineer is deciding between Dask and Apache Spark.
Which statement best describes a key difference between the two frameworks?
- A. Spark is better suited for structured and semi-structured data, while Dask excels at unstructured data processing.
- B. Dask is a more lightweight solution, often preferred for Python-centric workflows, whereas Spark provides broader ecosystem integrations and supports SQL-like operations natively.
- C. Spark cannot utilize GPUs, whereas Dask has built-in GPU acceleration by default.
- D. Dask is optimized for in-memory processing, while Spark requires disk-based storage for computation.
Answer: B
NEW QUESTION # 140
A data scientist is using an NVIDIA RAPIDS-based data processing pipeline on a GPU cluster. They notice that the pipeline is not performing as expected and suspect a bottleneck.
Which of the following approaches would best help identify the source of the bottleneck?
- A. Rely on nvidia-smi alone to check GPU utilization without considering other profiling tools.
- B. Disable GPU parallelism and run computations on the CPU to compare performance.
- C. Use nsys (NVIDIA Nsight Systems) to profile GPU kernel execution and memory transfer times.
- D. Increase the batch size of data being processed without analyzing memory utilization.
Answer: C
NEW QUESTION # 141
......
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