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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q169-Q174):
NEW QUESTION # 169
A data science team is deploying a deep learning model for real-time inference. The model is optimized for inference on an NVIDIA A100 GPU, but the team notices that inference latency is higher than expected.
Which of the following optimizations is most effective in reducing inference latency?
- A. Reduce the model size by randomly pruning neurons without retraining.
- B. Use mixed-precision inference with TensorRT to accelerate computation.
- C. Enable CPU offloading to balance the workload between the CPU and GPU.
- D. Increase the batch size significantly to improve GPU utilization.
Answer: B
NEW QUESTION # 170
You are working on a data science project that involves processing large-scale financial transaction data. You want to optimize data manipulation operations using NVIDIA's RAPIDS cuDF.
Which of the following approaches best leverages NVIDIA technologies for efficient data manipulation?
- A. Convert the dataset into a SQLite database and execute SQL queries to perform data transformations.
- B. Preprocess the data using Apache Spark's CPU-based DataFrame API before transferring it to a GPU for machine learning.
- C. Use cuDF DataFrames for data manipulation and rely on GPU-accelerated functions like .groupby(),
.merge(), and .applymap(). - D. Load the dataset into a Pandas DataFrame and use multi-threading to speed up operations.
Answer: C
NEW QUESTION # 171
A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?
- A. Using a single system-wide installation of CUDA and forcing all projects to use the same version
- B. Manually installing each package and its dependencies using pip
- C. Using only virtual environments (venv) without managing GPU dependencies separately
- D. Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies
Answer: D
NEW QUESTION # 172
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. Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.
- B. Store data in the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.
- C. Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.
- D. Store data in SQLite databases and query it into pandas before converting it to cuDF.
Answer: B
NEW QUESTION # 173
A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?
- A. Rely on Matplotlib to generate static plots and update them every minute with a loop.
- B. Precompute the time-series visualization with Dask and display it in a static HTML page.
- C. Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.
- D. Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.
Answer: D
NEW QUESTION # 174
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