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| Section | Weight | Objectives |
|---|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
- 1. GPU-accelerated ETL design and implementation
- 2. Data caching and performance optimization
- 3. Distributed data processing frameworks (Dask)
|
| MLOps | 19% | - Deployment and Monitoring
- 1. Memory and capacity evaluation
- 2. Model deployment in production environments
- 3. Performance benchmarking and optimization
|
| Machine Learning | 15% | - Model Development and Optimization
- 1. Multi-GPU training comparison
- 2. Hyperparameter tuning
- 3. Memory optimization techniques (mixed precision, batching)
- 4. Feature engineering
|
| Data Preparation | 17% | - Data Cleaning and Transformation
- 1. Data normalization and standardization
- 2. cuDF and pandas data preprocessing
- 3. Synthetic data generation with RAPIDS
|
| 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
|
| GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
- 1. Benchmarking GPU workflows
- 2. Docker and Conda environment management
- 3. CRISP-DM workflow execution
|
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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q160-Q165):
NEW QUESTION # 160
You are processing a large dataset with UNIX timestamps (seconds since Jan 1, 1970) ranging from Jan 1, 2000, to the present.
What is the most memory-efficient data type for the timestamp column in a GPU-accelerated cloud environment?
- A. df['timestamp'] = df['timestamp'].astype('int64')
- B. df['timestamp'] = df['timestamp'].astype('datetime64[ms]')
- C. df['timestamp'] = df['timestamp'].astype('int32')
- D. df['timestamp'] = df['timestamp'].astype('float32')
Answer: C
NEW QUESTION # 161
A data scientist wants to compare the performance of two different GPU-accelerated data science frameworks, NVIDIA RAPIDS (cuDF, cuML) and TensorFlow, for a tabular data classification task.
Which of the following approaches would be the best practice for designing an unbiased and effective benchmark?
- A. Ignore preprocessing and focus only on model training speed when comparing performance.
- B. Run all benchmarks on a CPU to ensure fairness across frameworks.
- C. Measure execution time and memory usage for each framework using NVIDIA Nsight Systems (nsys).
- D. Use TensorFlow's built-in training time metrics without comparing equivalent RAPIDS-based operations.
Answer: C
NEW QUESTION # 162
You are processing a large dataset in a distributed computing environment using RAPIDS and Dask.
Your workflow involves frequent shuffling of data between partitions, leading to significant slowdowns.
Which of the following strategies is the best way to implement data caching to reduce shuffle overhead using NVIDIA technologies?
- A. Use a CPU-based caching solution like Memcached to store intermediate data before reloading into cuDF.
- B. Disable caching altogether to force a recomputation of results, ensuring up-to-date data processing.
- C. Enable GPU-accelerated caching with RAPIDS cuDF and persist intermediate results in GPU memory.
- D. Use traditional disk-based caching by writing intermediate results to CSV files and reloading when needed.
Answer: C
NEW QUESTION # 163
A data science team is using NVIDIA GPUs to accelerate their AI workflow for a fraud detection system. They follow the CRISP-DM methodology to ensure a structured approach to the project.
During the Data Preparation phase, what is the most effective way to leverage NVIDIA technologies?
- A. Use NVIDIA RAPIDS to preprocess large-scale transaction datasets efficiently on GPUs before training the model.
- B. Perform all data transformation on a single CPU core to ensure stability before deploying to GPU- accelerated training.
- C. Manually clean and transform data using traditional CPU-based processing tools like pandas, then transfer the data to the GPU only for training.
- D. Ignore feature engineering and feed raw data directly into the model, relying on deep learning to extract relevant features.
Answer: A
NEW QUESTION # 164
You are working on a large-scale social network analysis project using cuGraph. Your goal is to identify the most influential individuals in the network based on their connectivity and reach.
Which of the following cuGraph algorithms would be the most appropriate choice for this task?
- A. Single Source Shortest Path (SSSP)
- B. Connected Components
- C. PageRank
- D. Louvain Clustering
Answer: C
NEW QUESTION # 165
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