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| Section | Weight | Objectives |
|---|
| MLOps | 19% | - Pipeline automation and orchestration - Monitoring, logging and maintenance - Model deployment and serving - End-to-end workflow management
|
| Data Manipulation and Software Literacy | 19% | - GPU-accelerated ETL workflows - Data processing libraries selection and usage - Dependency management and containerization - Performance profiling and optimization tools
|
| Data Analysis | 14% | - Time-series analysis and anomaly detection - Data visualization and graph analytics - Exploratory Data Analysis (EDA) - Distributed and parallel data processing
|
| Data Preparation | 17% | - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization - Data validation and quality assurance
|
| GPU and Cloud Computing | 16% | - CRISP-DM and data science methodology - GPU architecture and acceleration principles - Resource management and scaling strategies - Cloud GPU environments and deployment
|
| Machine Learning | 15% | - Model evaluation and validation - Distributed training strategies - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning
|
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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q87-Q92):
NEW QUESTION # 87
A machine learning team is handling large-scale datasets that need to be efficiently stored and accessed within an NVIDIA RAPIDS workflow.
Which of the following storage formats and techniques provides the best performance for GPU-based data science pipelines?
- A. Store data in SQLite databases and query it into pandas before converting it to cuDF.
- B. Store data in the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.
- C. Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.
- D. Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.
Answer: B
NEW QUESTION # 88
You are working with a large dataset in NVIDIA RAPIDS cuDF and notice that the data processing pipeline is taking longer than expected.
Which of the following tools or techniques can help you identify and analyze bottlenecks in the pipeline?
- A. Use cudf.DataFrame.astype(str) to convert numerical values to strings for easier debugging.
- B. Use cuml.preprocessing.StandardScaler() to normalize the dataset before processing.
- C. Use nvtx.annotate() to label sections of code and analyze performance in Nsight Systems.
- D. Enable pandas compatibility mode in cudf to improve performance.
Answer: C
NEW QUESTION # 89
You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?
- A. Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution
- B. Drop all missing values using .dropna() instead of filling them, as GPU memory is limited
- C. Use cuDF's .fillna() method to replace missing values in GPU memory
- D. Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF
Answer: C
NEW QUESTION # 90
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. Using a single storage location for both raw and transformed data to simplify the workflow.
- B. Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
- C. Ensuring the ETL pipeline uses only batch processing for data ingestion.
- D. Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.
- E. Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.
Answer: D,E
NEW QUESTION # 91
A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?
- A. Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
- B. Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.
- C. Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.
- D. Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
Answer: B
NEW QUESTION # 92
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