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[General] 高命中率的AIP-210題庫平臺 -最新的AIP-210認證新題庫已出

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【General】 高命中率的AIP-210題庫平臺 -最新的AIP-210認證新題庫已出

Posted at 12 hour before      View：13 | Replies：0        Print      Only Author   [Copy Link] 1#
順便提一下，可以從雲存儲中下載Testpdf AIP-210考試題庫的完整版：https://drive.google.com/open?id=1cyzbW5T_bLG0whSUnfrsHlnLHDwrdEWg
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最新的 Certified AI Practitioner AIP-210 免費考試真題 (Q50-Q55):問題 #50
Which of the following can benefit from deploying a deep learning model as an embedded model on edge devices?
  • A. A more complex model
  • B. Reduction in latency
  • C. Increase in data bandwidth consumption
  • D. Guaranteed availability of enough space
答案：B
解題說明：
Latency is the time delay between a request and a response. Latency can affect the performance and user experience of an application, especially when real-time or near-real-time responses are required. Deploying a deep learning model as an embedded model on edge devices can reduce latency, as the model can run locally on the device without relying on network connectivity or cloud servers. Edge devices are devices that are located at the edge of a network, such as smartphones, tablets, laptops, sensors, cameras, or drones.

問題 #51
Which of the following scenarios is an example of entanglement in ML pipelines?
  • A. Add a new method for drift detection in the model evaluation step.
  • B. Change the way output is visualized in the monitoring step.
  • C. Change in normalization function in the feature engineering step.
  • D. Add a new pipeline for retraining the model in the model training step.
答案：C
解題說明：
Explanation
Entanglement in ML pipelines occurs when a change in one step affects other steps that depend on it.
Changing the normalization function in the feature engineering step would affect the model training and evaluation steps, as they rely on the features generated by the feature engineering step. Therefore, this scenario is an example of entanglement in ML pipelines. The other scenarios are not examples of entanglement, as they do not affect other steps in the pipeline.

問題 #52
Which of the following sentences is TRUE about the definition of cloud models for machine learning pipelines?
  • A. Data as a Service (DaaS) can host the databases providing backups, clustering, and high availability.
  • B. Software as a Service (SaaS) can provide AI practitioner data science services such as Jupyter notebooks.
  • C. Infrastructure as a Service (IaaS) can provide CPU, memory, disk, network and GPU.
  • D. Platform as a Service (PaaS) can provide some services within an application such as payment applications to create efficient results.
答案：B
解題說明：
Cloud models are service models that provide different levels of abstraction and control over computing resources in a cloud environment. Some of the common cloud models for machine learning pipelines are:
* Software as a Service (SaaS): SaaS provides ready-to-use applications that run on the cloud provider's infrastructure and are accessible through a web browser or an API. SaaS can provide AI practitioner data science services such as Jupyter notebooks, which are web-based interactive environments that allow users to create and share documents that contain code, text, visualizations, and more.
* Platform as a Service (PaaS): PaaS provides a platform that allows users to develop, run, and manage applications without worrying about the underlying infrastructure. PaaS can provide some services within an application such as payment applications to create efficient results.
* Infrastructure as a Service (IaaS): IaaS provides access to fundamental computing resources such as servers, storage, networks, and operating systems. IaaS can provide CPU, memory, disk, network and GPU resources that can be used to run machine learning models and applications.
* Data as a Service (DaaS): DaaS provides access to data sources that can be consumed by applications or users on demand. DaaS can host the databases providing backups, clustering, and high availability.

問題 #53
Which of the following pieces of AI technology provides the ability to create fake videos?
  • A. Long short-term memory (LSTM) networks
  • B. Support-vector machines (SVM)
  • C. Recurrent neural networks (RNN)
  • D. Generative adversarial networks (GAN)
答案：D
解題說明：
Generative adversarial networks (GAN) are a type of AI technology that can create fake videos, images, audio, or text that are realistic and indistinguishable from real ones. GAN consist of two neural networks: a generator and a discriminator. The generator tries to produce fake samples from random noise, while the discriminator tries to distinguish between real and fake samples. The two networks compete against each other in a game-like scenario, where the generator tries to fool the discriminator and the discriminator tries to catch the generator. Through this process, both networks improve their abilities until they reach an equilibrium where the generator can produce convincing fakes.

問題 #54
When should the model be retrained in the ML pipeline?
  • A. A new monitoring component is added.
  • B. More data become available for the training phase.
  • C. Concept drift is detected in the pipeline.
  • D. Some outliers are detected in live data.
答案：C
解題說明：
When concept drift is detected in the pipeline, it means that the model performance has degraded over time due to changes in the underlying data generating process. This requires retraining the model with new data that reflects the current situation and updating the model parameters accordingly. References: Use pipeline parameters to retrain models in the designer - Azure Machine Learning | Microsoft Learn, Retraining Model During Deployment: Continuous Training and Continuous Testing

問題 #55
......
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