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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Machine Learning Engineer Certification Exam |
| Exam Number: | Professional-Machine-Learning-Engineer |
| Available Languages: | English, Japanese |
| Exam Format: | Multiple choice, Multiple select |
| Related Certifications: | Google Cloud Professional Data Engineer Google Cloud Professional Cloud Architect |
| Exam Price: | $200 USD (plus tax where applicable) |
| Passing Score: | Not officially published, approximately 70% |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 50-60 |
| Recommended Training: | Google Cloud Skills Boost - Professional Machine Learning Engineer Learning Path Official Exam Guide |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Machine-Learning-Engineer Sample Questions |
| Exam Way: | Online-proctored remote exam or onsite-proctored exam at authorized test centers |
| Pre Condition: | No mandatory prerequisites; recommended 3+ years industry experience including 1+ year designing/managing Google Cloud solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/machine-learning-engineer |
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Google Professional Machine Learning Engineer Certification Exam is a credential that is designed for professionals who are interested in building and deploying machine learning models using the Google Cloud Platform. Google Professional Machine Learning Engineer certification exam is an ideal choice for individuals who have experience in developing machine learning solutions, are familiar with cloud computing technologies, and are interested in pursuing a career in machine learning engineering.
The Google Professional Machine Learning Engineer certification exam is a great way for professionals to demonstrate their knowledge and skills in machine learning. It is also an excellent way for employers to identify and hire qualified candidates with advanced skills in machine learning. Google Professional Machine Learning Engineer certification exam is recognized by Google and is a valuable asset for professionals who want to advance their career in machine learning.
NEW QUESTION # 318
You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?
Answer: C
NEW QUESTION # 319
You are deploying a new version of a model to a production Vertex Al endpoint that is serving traffic. You plan to direct all user traffic to the new model. You need to deploy the model with minimal disruption to your application. What should you do?
Answer: A
NEW QUESTION # 320
You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company's weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter's published date and the user remains on the page for at least one minute.
All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model's performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?
Answer: D
Explanation:
The best option for monitoring the model to determine when retraining is necessary is to schedule a weekly query in BigQuery to compute the success metric. This option has the following advantages:
* It allows the model performance to be evaluated regularly, based on the actual outcome of the recommendations. By computing the success metric, which is the percentage of articles that are opened within two days and read for at least one minute, you can measure how well the model is achieving its objective and compare it with the acceptable baseline.
* It leverages the scalability and efficiency of BigQuery, which is a serverless, fully managed, and highly scalable data warehouse that can run complex queries over petabytes of data in seconds. By using BigQuery, you can access and analyze all the information needed to compute the success metric, such as the newsletter publication date, the article opening date, and the user reading time, without worrying about the infrastructure or the cost.
* It simplifies the model monitoring and retraining workflow, as the weekly query can be scheduled and executed automatically using BigQuery's built-in scheduling feature. You can also set up alerts or notifications to inform you when the success metric falls below the acceptable baseline, and trigger the model retraining process accordingly.
The other options are less optimal for the following reasons:
* Option A: Using Vertex AI Model Monitoring to detect skew of the input features with a sample rate of
100% and a monitoring frequency of two days introduces additional complexity and overhead. This option requires setting up and managing a Vertex AI Model Monitoring service, which is a managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. However, using Vertex AI Model Monitoring to detect skew of the input features may not reflect the actual performance of the model, as skew is the discrepancy between the distributions of the features in the training dataset and the serving data, which may not affect the outcome of the recommendations. Moreover, using a sample rate of 100% and a monitoring frequency of two days may incur unnecessary cost and latency, as it requires analyzing all the input features every two days, which may not be needed for the model monitoring.
* Option B: Scheduling a cron job in Cloud Tasks to retrain the model every week before the newsletter is created introduces additional cost and risk. This option requires creating and running a cron job in Cloud Tasks, which is a fully managed service that allows you to schedule and execute tasks that are invoked by HTTP requests. However, using Cloud Tasks to retrain the model every week may not be optimal, as it may retrain the model more often than necessary, wasting compute resources and cost.
Moreover, using Cloud Tasks to retrain the model before the newsletter is created may introduce risk, as it may deploy a new model version that has not been tested or validated, potentially affecting the quality of the recommendations.
* Option D: Scheduling a daily Dataflow job in Cloud Composer to compute the success metric introduces additional complexity and cost. This option requires creating and running a Dataflow job in Cloud Composer, which is a fully managed service that runs Apache Airflow pipelines for workflow orchestration. Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. However, using Dataflow and Cloud Composer to compute the success metric may not be necessary, as it may add more steps and overhead to the model monitoring process. Moreover, using Dataflow and Cloud Composer to compute the success metric daily may not be optimal, as it may compute the success metric more often than needed, consuming more compute resources and cost.
:
[BigQuery documentation]
[Vertex AI Model Monitoring documentation]
[Cloud Tasks documentation]
[Cloud Composer documentation]
[Dataflow documentation]
NEW QUESTION # 321
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?
Answer: A
Explanation:
A feature cross is a synthetic feature that is obtained by combining two or more existing features, usually by taking their product or concatenation. A feature cross can help to capture the nonlinear and interaction effects between the original features, and improve the predictive performance of the model. A feature cross can be applied to different types of features, such as numeric, categorical, or geospatial features1.
For the use case of building an ML model to predict car sales in different cities around the world, the best option is to use one feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type. This option involves creating a feature cross that combines three individual features: binned latitude, binned longitude, and one-hot encoded car type. Binning is a technique that transforms a continuous numeric feature into a discrete categorical feature by dividing its range into equal intervals, or bins. One-hot encoding is a technique that transforms a categorical feature into a binary vector, where each element corresponds to a possible category, and has a value of 1 if the feature belongs to that category, and 0 otherwise. By applying binning and one-hot encoding to the latitude, longitude, and car type features, the feature cross can capture the city-specific relationships between car type and number of sales, as each combination of bins and car types can represent a different city and its preference for a certain car type.
For example, the feature cross can learn that a city with a latitude bin of [40, 50], a longitude bin of [-80, -70], and a car type of SUV has a higher number of sales than a city with a latitude bin of [-10, 0], a longitude bin of [10, 20], and a car type of sedan. Therefore, using one feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type is the best option for this use case.
References:
* Feature Crosses | Machine Learning Crash Course
NEW QUESTION # 322
You have successfully deployed to production a large and complex TensorFlow model trained on tabular data.
You want to predict the lifetime value (LTV) field for each subscription stored in the BigQuery table named subscription. subscriptionPurchase in the project named my-fortune500-company-project.
You have organized all your training code, from preprocessing data from the BigQuery table up to deploying the validated model to the Vertex AI endpoint, into a TensorFlow Extended (TFX) pipeline. You want to prevent prediction drift, i.e., a situation when a feature data distribution in production changes significantly over time. What should you do?
Answer: A
Explanation:
* Option A is incorrect because implementing continuous retraining of the model daily using Vertex AI Pipelines is not the most efficient way to prevent prediction drift. Vertex AI Pipelines is a service that allows you to create and run scalable and portable ML pipelines on Google Cloud1. You can use Vertex AI Pipelines to retrain your model daily using the latest data from the BigQuery table. However, this option may be unnecessary or wasteful, as the data distribution may not change significantly every day, and retraining the model may consume a lot of resources and time. Moreover, this option does not monitor the model performance or detect the prediction drift, which are essential steps for ensuring the quality and reliability of the model.
* Option B is correct because adding a model monitoring job where 10% of incoming predictions are sampled 24 hours is the best way to prevent prediction drift. Model monitoring is a service that allows you to track the performance and health of your deployed models over time2. You can use model monitoring to sample a fraction of the incoming predictions and compare them with the ground truth labels, which can be obtained from the BigQuery table or other sources. You can also use model monitoring to compute various metrics, such as accuracy, precision, recall, or F1-score, and set thresholds or alerts for them. By using model monitoring, you can detect and diagnose the prediction drift, and decide when to retrain or update your model. Sampling 10% of the incoming predictions every 24 hours is a reasonable choice, as it balances the trade-off between the accuracy and the cost of the monitoring job.
* Option C is incorrect because adding a model monitoring job where 90% of incoming predictions are sampled 24 hours is not a optimal way to prevent prediction drift. This option has the same advantages as option B, as it uses model monitoring to track the performance and health of the deployed model.
However, this option is not cost-effective, as it samples a very large fraction of the incoming predictions, which may incur a lot of storage and processing costs. Moreover, this option may not improve the accuracy of the monitoring job significantly, as sampling 10% of the incoming predictions may already provide a representative sample of the data distribution.
* Option D is incorrect because adding a model monitoring job where 10% of incoming predictions are sampled every hour is not a necessary way to prevent prediction drift. This option also has the same advantages as option B, as it uses model monitoring to track the performance and health of the deployed model. However, this option may be excessive, as it samples the incoming predictions too frequently, which may not reflect the actual changes in the data distribution. Moreover, this option may incur more storage and processing costs than option B, as it generates more samples and metrics.
References:
* Vertex AI Pipelines documentation
* Model monitoring documentation
* [Prediction drift]
* [TensorFlow Extended documentation]
* [BigQuery documentation]
* [Vertex AI documentation]
NEW QUESTION # 323
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