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To become a certified Google Professional Machine Learning Engineer, candidates must demonstrate proficiency in several key areas, including data preparation and processing, machine learning model development and implementation, and business problem-solving. Professional-Machine-Learning-Engineer Exam covers a broad range of topics, including data modeling, distributed computing, and machine learning frameworks such as TensorFlow and Keras. Additionally, candidates must be familiar with the tools and technologies used in machine learning, including Google Cloud Platform services such as BigQuery, Dataflow, and Kubernetes.

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Google Professional Machine Learning Engineer Sample Questions (Q229-Q234):

NEW QUESTION # 229
Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

A. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.
2 Dispatch an appropriately sized shuttle and indicate the required stops on the map
B. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.
2. Dispatch an available shuttle and provide the map with the required stops based on the prediction
C. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.
D. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

Answer: C

Explanation:
This answer is correct because it uses a regression model to estimate the number of passengers at each shuttle station, which is a continuous variable. A tree-based regression model can handle both numerical and categorical features, such as the time of day, the location of the station, and the weather conditions. Based on the predicted number of passengers, the organization can dispatch a shuttle that has enough capacity and provide a map that shows the required stops. This way, the organization can optimize the shuttle service route and reduce the waiting time and fuel consumption. References:
* [Tree-based regression models]

NEW QUESTION # 230
You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

A. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.
B. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.
C. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
D. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.

Answer: A

Explanation:
Data Catalog is a fully managed and scalable metadata management service that allows you to quickly discover, manage, and understand your data in Google Cloud. You can use Data Catalog to search the BigQuery datasets by using keywords in the table description, as well as other metadata attributes such as table name, column name, labels, tags, and more. Data Catalog also provides a rich browsing experience that lets you explore the schema, preview the data, and access the BigQuery console directly from the Data Catalog UI.
Data Catalog helps you find the data that you need for your model building on AI Platform without writing any code or queries.
References:
* [Data Catalog documentation]
* [Data Catalog overview]
* [Searching for data assets]

NEW QUESTION # 231
You work for a retail company. You have created a Vertex AI forecast model that produces monthly item sales predictions. You want to quickly create a report that will help to explain how the model calculates the predictions. You have one month of recent actual sales data that was not included in the training dataset. How should you generate data for your report?

A. Generate counterfactual examples by using the actual sales data. Create a batch prediction job using the actual sales data and the counterfactual examples. Compare the results in the report.
B. Train another model by using the same training dataset as the original, and exclude some columns. Using the actual sales data create one batch prediction job by using the new model and another one with the original model. Compare the two sets of predictions in the report.
C. Create a batch prediction job by using the actual sales data, and configure the job settings to generate feature attributions. Compare the results in the report.
D. Create a batch prediction job by using the actual sales data. Compare the predictions to the actuals in the report.

Answer: C

NEW QUESTION # 232
You work for a manufacturing company. You need to train a custom image classification model to detect product detects 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 models results. What should you do?

A. Configure feature-based explanations by using sampled Shapley. Set number of feature permutations to the maximum value of 50.
B. Configure example-based explanations. Specify the embedding output layer to be used for the latent space representation.
C. Create an index by using Vertex Al Matching Engine. Query the index with your mislabeled images
D. Configure example-based explanations by using integrated gradients. Set visualization type to pixels, and set clip_percent_upperbound to 95.

Answer: D

NEW QUESTION # 233
You are developing an ML model to predict house prices. While preparing the data, you discover that an important predictor variable, distance from the closest school, is often missing and does not have high variance. Every instance (row) in your data is important. How should you handle the missing data?

A. Predict the missing values using linear regression.
B. Apply feature crossing with another column that does not have missing values.
C. Replace the missing values with zeros.
D. Delete the rows that have missing values.

Answer: A

Explanation:
The best option for handling missing data in this case is to predict the missing values using linear regression.
Linear regression is a supervised learning technique that can be used to estimate the relationship between a continuous target variable and one or more predictor variables. In this case, the target variable is the distance from the closest school, and the predictor variables are the other features in the dataset, such as house size, location, number of rooms, etc. By fitting a linear regression model on the data that has no missing values, we can then use the model to predict the missing values for the distance from the closest school feature. This way, we can preserve all the instances in the dataset and avoid introducing bias or reducing variance. The other options are not suitable for handling missing data in this case, because:
* Deleting the rows that have missing values would reduce the size of the dataset and potentially lose important information. Since every instance is important, we want to keep as much data as possible.
* Applying feature crossing with another column that does not have missing values would create a new feature that combines the values of two existing features. This might increase the complexity of the model and introduce noise or multicollinearity. It would not solve the problem of missing values, as the new feature would still have missing values whenever the distance from the closest school feature is missing.
* Replacing the missing values with zeros would distort the distribution of the feature and introduce bias.
It would also imply that the houses with missing values are located at the same distance from the closest school, which is unlikely to be true. A zero value might also be outside the range of the feature, as the distance from the closest school is unlikely to be exactly zero for any house. References:
* Linear Regression
* Imputation of missing values
* Google Cloud launches machine learning engineer certification
* Google Professional Machine Learning Engineer Certification
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 234
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