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1. You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead.

What should you do?

Export the model to BigQuery M

Deploy and version the model on Al Platform.

Use Dataflow with the SavedModel to read the data from BigQuery

Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.

2. You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated.

What issue is most likely causing the steady decline in model accuracy?

Poor data quality

Lack of model retraining

Too few layers in the model for capturing information

Incorrect data split ratio during model training, evaluation, validation, and test

3. You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories.

What should you do?

Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run

Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.

Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.

Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic

4. You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory data. Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production.

What is the most streamlined and reliable way to perform this validation?

Use the TFX ModelValidator tools to specify performance metrics for production readiness

Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.

Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data

Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.

5. You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results.

What should you do?

Extract sentiment directly from the voice recordings

Convert the speech to text and build a model based on the words

Convert the speech to text and extract sentiments based on the sentences

Convert the speech to text and extract sentiment using syntactical analysis

6. You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory.

How should you create a dataset following Google-recommended best practices?

Create a tf.data.Dataset.prefetch transformation

Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).

Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().

Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training

7. You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible.

What should you do?

Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.

Write a Python script that uses the BigQuery API to execute queries against BigQuery Execute this script as the first step in your Kubeflow pipeline

Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries

Locate the Kubeflow Pipelines repository on GitHub Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery

8. 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?

Create a new view with BigQuery that does not include a column with city information

Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.

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.

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 M

9. Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data.

How should you address the input differences in production?

Create alerts to monitor for skew, and retrain the model.

Perform feature selection on the model, and retrain the model with fewer features

Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service

Perform feature selection on the model, and retrain the model on a monthly basis with fewer features

10. You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure.

Which additional readiness check should you recommend to the team?

Ensure that training is reproducible

Ensure that all hyperparameters are tuned

Ensure that model performance is monitored

Ensure that feature expectations are captured in the schema

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11. You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time.

How should you build the model?

Use the Al Platform Training built-in algorithms to create a custom model

Use AutoML Natural Language to extract custom entities for classification

Use the Cloud Natural Language API to extract custom entities for classification

Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm

12. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

13. 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?

Three individual features binned latitude, binned longitude, and one-hot encoded car type

One feature obtained as an element-wise product between latitude, longitude, and car type

One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type

Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type

14. You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime.

How should you perform this comparison?

Compare the loss performance for each model on a held-out dataset.

Compare the loss performance for each model on the validation data

Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool

Compare the mean average precision across the models using the Continuous Evaluation feature

15. You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction.

Which architecture should you use?

• Validate the accuracy of the model that you trained on preprocessed data • Create a new model that uses the raw data and is available in real time • Deploy the new model onto Al Platform for online prediction

• Send incoming prediction requests to a Pub/Sub topic • Transform the incoming data using a Dataflow job • Submit a prediction request to Al Platform using the transformed data • Write the predictions to an outbound Pub/Sub queue

• Stream incoming prediction request data into Cloud Spanner • Create a view to abstract your preprocessing logic. • Query the view every second for new records • Submit a prediction request to Al Platform using the transformed data • Write the predictions to an outbound Pub/Sub queue.

• Send incoming prediction requests to a Pub/Sub topic • Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic. • Implement your preprocessing logic in the Cloud Function • Submit a prediction request to Al Platform using the transformed data • Write the predictions to an outbound Pub/Sub queue

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