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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. In a typical MLOps pipeline, which of the following practices are essential to ensuring robust deployment and monitoring of machine learning models in production? (Select two)

A) Post-deployment data drift detection to assess model performance degradation.
B) Use of manual intervention for every model update to ensure accuracy.
C) Continuous integration and continuous deployment (CI/CD) pipelines for model updates.
D) Automated hyperparameter tuning during inference to optimize model performance.

2. A data scientist is deploying a deep learning model to production using an NVIDIA GPU-powered inference server. They want to ensure low latency and high throughput while maintaining model accuracy.
Which of the following deployment strategies would be most effective?

A) Deploying a model without batching to ensure real-time responses
B) Using NVIDIA Triton Inference Server with model ensemble optimization
C) Disabling mixed-precision inference to avoid accuracy loss
D) Running inference on a CPU instead of a GPU to reduce power consumption

3. In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A) Efficient handling of matrix operations in machine learning models
B) Lower cost of cloud infrastructure
C) Lower energy consumption compared to CPUs
D) Faster parallel processing for large datasets
E) Better for memory-intensive workloads

4. A data scientist is processing a dataset that is too large to fit into the memory of a single GPU. They decide to use Dask with cuDF to leverage multiple GPUs for accelerated computation.
Which of the following approaches ensures efficient parallelism when working with dask_cudf?

A) Use Dask futures to asynchronously submit GPU tasks and collect results when completed.
B) Use a single-threaded scheduler to avoid contention when running on multiple GPUs.
C) Convert the dataset into a Dask cuDF dataframe and use persist() to keep it in GPU memory.
D) Assign explicit chunks of data to each worker by manually dividing the dataset before passing it to Dask.

5. You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?

A) df = cudf.to_gpu(pd.read_parquet("data.parquet"))
B) df = cudf.read_parquet("data.parquet")
C) df = pd.read_parquet("data.parquet")
D) df = cudf.read_csv("data.parquet")

Solutions: