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Google Professional Machine Learning Engineer Sample Questions (Q11-Q16):

NEW QUESTION # 11
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?

A. Categorical cross-entropy
B. Categorical hinge
C. Binary cross-entropy
D. Sparse categorical cross-entropy

Answer: D

Explanation:
se sparse_categorical_crossentropy. Examples for above 3-class classification problem: [1] , [2], [3]

NEW QUESTION # 12
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 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.
B. 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.
C. 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.
D. Use the create model statement and select the categorical and non-categorical features.

Answer: B

Explanation:
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 values1. 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 data2. 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 model3.
* 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 Learning 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

NEW QUESTION # 13
You work for a retail company. You have a managed tabular dataset in Vertex Al that contains sales data from three different stores. The dataset includes several features such as store name and sale timestamp. You want to use the data to train a model that makes sales predictions for a new store that will open soon You need to split the data between the training, validation, and test sets What approach should you use to split the data?

A. Use Vertex Al random split assigning 70% of the rows to the training set, 10% to the validation set, and
20% to the test set.
B. Use Vertex Al default data split.
C. Use Vertex Al manual split, using the store name feature to assign one store for each set.
D. Use Vertex Al chronological split and specify the sales timestamp feature as the time vanable.

Answer: B

Explanation:
The best option for splitting the data between the training, validation, and test sets, using a managed tabular dataset in Vertex AI that contains sales data from three different stores, is to use Vertex AI default data split.
This option allows you to leverage the power and simplicity of Vertex AI to automatically and randomly split your data into the three sets by percentage. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can support various types of models, such as linear regression, logistic regression, k-means clustering, matrix factorization, and deep neural networks. Vertex AI can also provide various tools and services for data analysis, model development, model deployment, model monitoring, and model governance. A default data split is a data split method that is provided by Vertex AI, and does not require any user input or configuration. A default data split can help you split your data into the training, validation, and test sets by using a random sampling method, and assign a fixed percentage of the data to each set. A default data split can help you simplify the data split process, and works well in most cases.
A training set is a subset of the data that is used to train the model, and adjust the model parameters. A training set can help you learn the relationship between the input features and the target variable, and optimize the model performance. A validation set is a subset of the data that is used to validate the model, and tune the model hyperparameters. A validation set can help you evaluate the model performance on unseen data, and avoid overfitting or underfitting. A test set is a subset of the datathat is used to test the model, and provide the final evaluation metrics. A test set can help you assess the model performance on new data, and measure the generalization ability of the model. By using Vertex AI default data split, you can split your data into the training, validation, and test sets by using a random sampling method, and assign the following percentages of the data to each set1:

The other options are not as good as option B, for the following reasons:
* Option A: Using Vertex AI manual split, using the store name feature to assign one store for each set would not allow you to split your data into representative and balanced sets, and could cause errors or poor performance. A manual split is a data split method that allows you to control how your data is split into sets, by using the ml_use label or the data filter expression. A manual split can help you customize the data split logic, and handle complex or non-standard data formats. A store name feature is a feature that indicates the name of the store where the sales data was collected. A store name feature can help you identify the source of the data, and group the data by store. However, using Vertex AI manual split, using the store name feature to assign one store for each set would not allow you to split your data into representative and balanced sets, and could cause errors or poor performance. You would need to write
* code, create and configure the ml_use label or the data filter expression, and assign one store for each set. Moreover, this option would not ensure that the data in each set has the same distribution and characteristics as the data in the whole dataset, which could prevent you from learning the general pattern of the data, and cause bias or variance in the model2.
* Option C: Using Vertex AI chronological split and specifying the sales timestamp feature as the time variable would not allow you to split your data into representative and balanced sets, and could cause errors or poor performance. A chronological split is a data split method that allows you to split your data into sets based on the order of the data. A chronological split can help you preserve the temporal dependency and sequence of the data, and avoid data leakage. A sales timestamp feature is a feature that indicates the date and time when the sales data was collected. A sales timestamp feature can help you track the changes and trends of the data over time, and capture the seasonality and cyclicality of the data. However, using Vertex AI chronological split and specifying the sales timestamp feature as the time variable would not allow you to split your data into representative and balanced sets, and could cause errors or poor performance. You would need to write code, create and configure the time variable, and split the data by the order of the time variable. Moreover, this option would not ensure that the data in each set has the same distribution and characteristics as the data in the whole dataset, which could prevent you from learning the general pattern of the data, and cause bias or variance in the model3.
* Option D: Using Vertex AI random split, assigning 70% of the rows to the training set, 10% to the validation set, and 20% to the test set would not allow you to use the default data splitmethod that is provided by Vertex AI, and could increase the complexity and cost of the data split process. A random split is a data split method that allows you to split your data into sets by using a random sampling method, and assign a custom percentage of the data to each set. A random split can help you split your data into representative and balanced sets, and avoid data leakage. However, using Vertex AI random split, assigning 70% of the rows to the training set, 10% to the validation set, and 20% to the test set would not allow you to use the default data split method that is provided by Vertex AI, and could increase the complexity and cost of the data split process. You would need to write code, create and configure the random split method, and assign the custom percentages to each set. Moreover, this option would not use the default data split method that is provided by Vertex AI, which can simplify the data split process, and works well in most cases1.
References:
* About data splits for AutoML models | Vertex AI | Google Cloud
* Manual split for unstructured data
* Mathematical split

NEW QUESTION # 14
You recently trained a XGBoost model that you plan to deploy to production for online inference Before sending a predict request to your model's binary you need to perform a simple data preprocessing step This step exposes a REST API that accepts requests in your internal VPC Service Controls and returns predictions You want to configure this preprocessing step while minimizing cost and effort What should you do?

A. Store a pickled model in Cloud Storage Build a Flask-based app packages the app in a custom container image, and deploy the model to Vertex Al Endpoints.
B. Build a custom predictor class based on XGBoost Predictor from the Vertex Al SDK and package the handler in a custom container image based on a Vertex built-in container image Store a pickled model in Cloud Storage and deploy the model to Vertex Al Endpoints.
C. Build a custom predictor class based on XGBoost Predictor from the Vertex Al SDK. package it and a pickled model in a custom container image based on a Vertex built-in image, and deploy the model to Vertex Al Endpoints.
D. Build a Flask-based app. package the app and a pickled model in a custom container image, and deploy the model to Vertex Al Endpoints.

Answer: B

Explanation:
* Option A is not the best answer because it requires storing the pickled model in Cloud Storage, which may incur additional cost and latency for loading the model. It also requires building a Flask-based app, which may not be necessary for a simple data preprocessing step.
* Option B is not the best answer because it requires building a Flask-based app, which may not be necessary for a simple data preprocessing step. It also requires packaging the app andthe pickled model
* in a custom container image, which may increase the size and complexity of the image.
* Option C is not the best answer because it requires packaging the pickled model in a custom container image, which may increase the size and complexity of the image. It also does not leverage the Vertex built-in container image, which may provide some optimizations and integrations for XGBoost models.
* Option D is the best answer because it leverages the Vertex built-in container image, which may provide some optimizations and integrations for XGBoost models. It also allows storing the pickled model in Cloud Storage, which may reduce the size and complexity of the image. It also allows building a custom predictor class based on XGBoost Predictor from the Vertex AI SDK, which may simplify the data preprocessing step and the prediction logic.

NEW QUESTION # 15
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?

A. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
B. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model
C. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
D. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery

Answer: C

Explanation:
(https://www.kubeflow.org/docs/components/pipelines/overview/pipelines-overview/
https://medium.com/google-cloud/how-to-build-an-end-to-end-propensity-to-purchase-solution-using-bigquery-ml-and-kubeflow-pipelines-cd4161f734d9#75c7

NEW QUESTION # 16
......

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