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Question 211

You work on an operations team at an international company that manages a large fleet of on-premises servers located in few data centers around the world. Your team collects monitoring data from the servers, including CPU/memory consumption. When an incident occurs on a server, your team is responsible for fixing it. Incident data has not been properly labeled yet. Your management team wants you to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. What should you do first?

A.Hire a team of qualified analysts to review and label the machines' historical performance data. Train a model based on this manually labeled dataset.
B.Implement a simple heuristic (e.g., based on z-score) to label the machines' historical performance data. Train a model to predict anomalies based on this labeled dataset.
C.Develop a simple heuristic (e.g., based on z-score) to label the machines' historical performance data. Test this heuristic in a production environment.
D.Train a time-series model to predict the machines' performance values. Configure an alert if a machine's actual performance values significantly differ from the predicted performance values.

Question 212

You are working with a dataset that contains customer transactions. You need to build an ML model to predict customer purchase behavior You plan to develop the model in BigQuery ML, and export it to Cloud Storage for online prediction You notice that the input data contains a few categorical features, including product category and payment method You want to deploy the model as quickly as possible. What should you do?

A.Use the transform clause with the ML. ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features.
B.Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.
C.Use the create model statement and select the categorical and non-categorical features.
D.Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.

Correct Answer: A

The best option for building an ML model to predict customer purchase behavior in BigQuery ML is to use the transform clause with the ML.ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features. This option allows you to encode the categorical features as one-hot vectors, which are binary vectors that have only one non-zero element. One- hot encoding is a common technique for handling categorical features in ML models, as it can reduce the dimensionality and sparsity of the data, and avoid the ordinality problem that arises when using numerical labels for categorical values 1 . The transform clause is a feature of BigQuery ML that lets you apply SQL expressions to transform the input data at model creation time. The transform clause can perform feature engineering, such as one-hot encoding, on the fly, without requiring you to create and store a new table with the transformed data 2 . By using the transform clause with the ML.ONE_HOT_ENCODER function, you can create and train an ML model in BigQuery ML with a single SQL statement, and export it to Cloud Storage for online prediction.
The other options are not as good as option A, for the following reasons:
* Option B: Using the ML.ONE_HOT_ENCODER function on the categorical features, and selecting the encoded categorical features and non-categorical features as inputs to create your model, would require more steps and storage than using the transform clause. The ML.ONE_HOT_ENCODER function is a BigQuery ML function that returns a one-hot encoded vector for a given categorical value. However, using this function alone would not apply the one-hot encoding to the input data at model creation time.
You would need to create a new table with the encoded features, and use that table as the input to create your model. This would incur additional storage costs and reduce the performance of the queries.
* Option C: Using the create model statement and selecting the categorical and non-categorical features, would not handle the categorical features properly and could result in a poor model performance. The create model statement is a BigQuery ML statement that creates and trains an ML model from a SQL query. However, if the input data contains categorical features, you need to encode them as one-hot vectors or use the category_count option to specify the number of categories for each feature. Otherwise, BigQuery ML would treat the categorical features as numerical values, which can introduce bias and noise into the model 3 .
* Option D: Using the ML.ONE_HOT_ENCODER function on the categorical features, and selecting the encoded categorical features and non-categorical features as inputs to create your model, is the same as option B, and has the same drawbacks.
References:
Preparing for Google Cloud Certification: Machine Le arning Engineer , Course 2: Data Engineering for ML on Google Cloud, Week 2: Feature Engineering Google Cloud Professional Machine Learning Engineer Exam Guide , Section 1: Architecting low-code ML solutions, 1.1 Developing ML models by using BigQuery ML Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 3: Data Engineering for ML, Section 3.2: BigQuery for ML One-hot encoding Using the TRANSFORM clause for feature engineering Creating a model ML.ONE_HOT_ENCODER function

Question 213

Your team is training a large number of ML models that use different algorithms, parameters and datasets. Some models are trained in Vertex Ai Pipelines, and some are trained on Vertex Al Workbench notebook instances. Your team wants to compare the performance of the models across both services. You want to minimize the effort required to store the parameters and metrics What should you do?

A.Implement an additional step for all the models running in pipelines and notebooks to export parameters and metrics to BigQuery.
B.Create a Vertex Al experiment Submit all the pipelines as experiment runs. For models trained on notebooks log parameters and metrics by using the Vertex Al SDK.
C.Implement all models in Vertex Al Pipelines Create a Vertex Al experiment, and associate all pipeline runs with that experiment.
D.Store all model parameters and metrics as mode! metadata by using the Vertex Al Metadata API.

Question 214

You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

A.Use Al Platform for distributed training
B.Create a cluster on Dataproc for training
C.Create a Managed Instance Group with autoscaling
D.Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.

Question 215

You are building a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

A.Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.
B.Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.
C.Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
D.Create a new view with BigQuery that does not include a column with city information