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Google Professional Machine Learning Engineer exam is a certification test that is designed to validate the skills and knowledge of individuals in the field of machine learning. Professional-Machine-Learning-Engineer Exam is intended for individuals who have a strong understanding of machine learning concepts, including supervised learning, unsupervised learning, and deep learning. Additionally, this certification exam assesses an individual's ability to design and implement machine learning models on the Google Cloud Platform.

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Google Professional Machine Learning Engineer Sample Questions (Q182-Q187):

NEW QUESTION # 182
A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.
Which approach should the Specialist use to continue working?

A. Install Python 3 and boto3 on their laptop and continue the code development using that environment.
B. Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.
C. Download the SageMaker notebook to their local environment, then install Jupyter Notebooks on their laptop and continue the development in a local notebook.
D. Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.

Answer: A

Explanation:
Explanation

NEW QUESTION # 183
You built a deep learning-based image classification model by using on-premises data. You want to use Vertex Al to deploy the model to production Due to security concerns you cannot move your data to the cloud. You are aware that the input data distribution might change over time You need to detect model performance changes in production. What should you do?

A. Create a Vertex Al Model Monitoring job. Enable training-serving skew detection for your model.
B. Create a Vertex Al Model Monitoring job. Enable feature attribution skew and dnft detection for your model.
C. Use Vertex Explainable Al for model explainability Configure example-based explanations.
D. Use Vertex Explainable Al for model explainability Configure feature-based explanations.

Answer: A

Explanation:
Vertex AI Model Monitoring is a service that allows you to monitor the performance and quality of your ML models in production. You can use Vertex AI Model Monitoring to detect changes in the input data distribution, the prediction output distribution, or the model accuracy over time. Training-serving skew detection is a feature of Vertex AI Model Monitoring that compares the statistics of the data used for training the model and the data used for serving the model. If there is a significant difference between the two data distributions, it indicates that the model might be outdated or inaccurate. By enabling training-serving skew detection for your model, you can detect model performance changes in production and trigger retraining or redeployment of your model as needed. This way, you can ensure that your model is always up-to-date and accurate, without moving your data to the cloud. References:
* Vertex AI Model Monitoring documentation
* Training-serving skew detection documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 184
You received a training-serving skew alert from a Vertex Al Model Monitoring job running in production. You retrained the model with more recent training data, and deployed it back to the Vertex Al endpoint but you are still receiving the same alert. What should you do?

A. Temporarily disable the alert Enable the alert again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint.
B. Update the model monitoring job to use a lower sampling rate.
C. Update the model monitoring job to use the more recent training data that was used to retrain the model.
D. Temporarily disable the alert until the model can be retrained again on newer training data Retrain the model again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint

Answer: C

Explanation:
The best option for resolving the training-serving skew alert is to update the model monitoring job to use the more recent training data that was used to retrain the model. This option can help align the baseline distribution of the model monitoring job with the current distribution of the production data, and eliminate the false positive alerts. Model Monitoring is a service that can track and compare the results of multiple machine learning runs. Model Monitoring can monitor the model's prediction input data for feature skew and drift. Training-serving skew occurs when the feature data distribution in production deviates from the feature data distribution used to train the model. If the original training data is available, you can enable skew detection to monitor your models for training-serving skew. Model Monitoring uses TensorFlow Data Validation (TFDV) to calculate the distributions and distance scores for each feature, and compares them with a baseline distribution. The baseline distribution is the statistical distribution of the feature's values in the training data. If the distance score for a feature exceeds an alerting threshold that you set, Model Monitoring sends you an email alert. However, if you retrain the model with more recent training data, and deploy it back to the Vertex AI endpoint, the baseline distribution of the model monitoring job may become outdated and inconsistent with the current distribution of the production data. This can cause the model monitoring job to generate false positive alerts, even if the model performance is not deteriorated. To avoid this problem, you need to update the model monitoring job to use the more recent training data that was used to retrain the model. This can help the model monitoring job to recalculate the baseline distribution and the distance scores, and compare them with the current distribution of the production data. This can also help the model monitoring job to detect any true positive alerts, such as a sudden change in the production data that causes the model performance to degrade1.
The other options are not as good as option B, for the following reasons:
Option A: Updating the model monitoring job to use a lower sampling rate would not resolve the training-serving skew alert, and could reduce the accuracy and reliability of the model monitoring job. The sampling rate is a parameter that determines the percentage of prediction requests that are logged and analyzed by the model monitoring job. Using a lower sampling rate can reduce the storage and computation costs of the model monitoring job, but also the quality and validity of the data. Using a lower sampling rate can introduce sampling bias and noise into the data, and make the model monitoring job miss some important features or patterns of the data. Moreover, using a lower sampling rate would not address the root cause of the training-serving skew alert, which is the mismatch between the baseline distribution and the current distribution of the production data2.
Option C: Temporarily disabling the alert, and enabling the alert again after a sufficient amount of new production traffic has passed through the Vertex AI endpoint, would not resolve the training-serving skew alert, and could expose the model to potential risks and errors. Disabling the alert would stop the model monitoring job from sending email notifications when the distance score for a feature exceeds the alerting threshold, but it would not stop the model monitoring job from calculating and comparing the distributions and distance scores. Therefore, disabling the alert would not address the root cause of the training-serving skew alert, which is the mismatch between the baseline distribution and the current distribution of the production data. Moreover, disabling the alert would prevent the model monitoring job from detecting any true positive alerts, such as a sudden change in the production data that causes the model performance to degrade. This can expose the model to potential risks and errors, and affect the user satisfaction and trust1.
Option D: Temporarily disabling the alert until the model can be retrained again on newer training data, and retraining the model again after a sufficient amount of new production traffic has passed through the Vertex AI endpoint, would not resolve the training-serving skew alert, and could cause unnecessary costs and efforts. Disabling the alert would stop the model monitoring job from sending email notifications when the distance score for a feature exceeds the alerting threshold, but it would not stop the model monitoring job from calculating and comparing the distributions and distance scores. Therefore, disabling the alert would not address the root cause of the training-serving skew alert, which is the mismatch between the baseline distribution and the current distribution of the production data. Moreover, disabling the alert would prevent the model monitoring job from detecting any true positive alerts, such as a sudden change in the production data that causes the model performance to degrade. This can expose the model to potential risks and errors, and affect the user satisfaction and trust. Retraining the model again on newer training data would create a new model version, but it would not update the model monitoring job to use the newer training data as the baseline distribution. Therefore, retraining the model again on newer training data would not resolve the training-serving skew alert, and could cause unnecessary costs and efforts1.
Reference:
Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 4: Evaluation Google Cloud Professional Machine Learning Engineer Exam Guide, Section 3: Scaling ML models in production, 3.3 Monitoring ML models in production Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6: Production ML Systems, Section 6.3: Monitoring ML Models Using Model Monitoring Understanding the score threshold slider Sampling rate

NEW QUESTION # 185
You work for a manufacturing company. You need to train a custom image classification model to detect product defects at the end of an assembly line Although your model is performing well some images in your holdout set are consistently mislabeled with high confidence You want to use Vertex Al to understand your model's results What should you do?

Answer: A

Explanation:
Vertex Explainable AI is a set of tools and frameworks to help you understand and interpret predictions made by your machine learning models, natively integrated with a number of Google's products and services1. With Vertex Explainable AI, you can generate feature-based explanations that show how much each input feature contributed to the model's prediction2. This can help you debug and improve your model performance, and build confidence in your model's behavior. Feature-based explanations are supported for custom image classification models deployed on Vertex AI Prediction3. Reference:
Explainable AI | Google Cloud
Introduction to Vertex Explainable AI | Vertex AI | Google Cloud
Supported model types for feature-based explanations | Vertex AI | Google Cloud

NEW QUESTION # 186
Your work for a textile manufacturing company. Your company has hundreds of machines and each machine has many sensors. Your team used the sensory data to build hundreds of ML models that detect machine anomalies Models are retrained daily and you need to deploy these models in a cost-effective way. The models must operate 24/7 without downtime and make sub millisecond predictions. What should you do?

A. Deploy a Dataflow batch pipeline with the Runlnference API. and use model refresh.
B. Deploy a Dataflow streaming pipeline with the Runlnference API and use automatic model refresh.
C. Deploy a Dataflow batch pipeline and a Vertex Al Prediction endpoint.
D. Deploy a Dataflow streaming pipeline and a Vertex Al Prediction endpoint with autoscaling.

Answer: B

Explanation:
A Dataflow streaming pipeline is a cost-effective way to process large volumes of real-time data from sensors.
The RunInference API is a Dataflow transform that allows you to run online predictions on your streaming data using your ML models. By using the RunInference API, you can avoid the latency and cost of using a separate prediction service. The automatic model refresh feature enables you to update your models in the pipeline without redeploying the pipeline. This way, you can ensure that your models are always up-to-date and accurate. By deploying a Dataflow streaming pipeline with the RunInference API and using automatic model refresh, you can achieve sub-millisecond predictions, 24/7 availability, and low operational overhead for your ML models. References:
* Dataflow documentation
* RunInference API documentation
* Automatic model refresh documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 187
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