Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) Free Practice Test

Question 1

You work at a large organization that recently decided to move their ML and data workloads to Google Cloud. The data engineering team has exported the structured data to a Cloud Storage bucket in Avro format. You need to propose a workflow that performs analytics, creates features, and hosts the features that your ML models use for online prediction. How should you configure the pipeline?

A. Ingest the Avro files into BigQuery to perform analytics. Use a Dataflow pipeline to create the features, and store them in Vertex AI Feature Store for online prediction.

B. Ingest the Avro files into BigQuery to perform analytics. Use BigQuery SQL to create features and store them in a separate BigQuery table for online prediction.

C. Ingest the Avro files into Cloud Spanner to perform analytics. Use a Dataflow pipeline to create the features, and store them in Vertex AI Feature Store for online prediction.

D. Ingest the Avro files into Cloud Spanner to perform analytics. Use a Dataflow pipeline to create the features, and store them in BigQuery for online prediction.

Correct Answer: A

Question 2

You work on a growing team of more than 50 data scientists who all use AI Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way.
Which strategy should you choose?

A. Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.

B. Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.

C. Use labels to organize resources into descriptive categories.
Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.

D. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using

Correct Answer: C

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Question 3

You are training an ML model on a large dataset. You are using a TPU to accelerate the training process. You notice that the training process is taking longer than expected. You discover that the TPU is not reaching its full capacity. What should you do?

A. Increase the batch size

B. Increase the learning rate

C. Increase the number of epochs

D. Decrease the learning rate

Correct Answer: A

Question 4

You are developing an ML model in a Vertex AI Workbench notebook. You want to track artifacts and compare models during experimentation using different approaches. You need to rapidly and easily transition successful experiments to production as you iterate on your model implementation. What should you do?

A. 1. Create a Vertex AI pipeline. Use the Dataset and Model artifact types from the Kubeflow Pipelines DSL as the inputs and outputs of the components in your pipeline.
2. In your training component, use the Vertex AI SDK to create an experiment run. Configure the log_params and log_metrics functions to track parameters and metrics of your experiment.

B. 1. Initialize the Vertex SDK with the name of your experiment. Log parameters and metrics for each experiment, and attach dataset and model artifacts as inputs and outputs to each execution.
2. After a successful experiment create a Vertex AI pipeline.

C. 1. Initialize the Vertex SDK with the name of your experiment. Log parameters and metrics for each experiment, save your dataset to a Cloud Storage bucket, and upload the models to Vertex AI Model Registry.
2. After a successful experiment, create a Vertex AI pipeline.

D. 1. Create a Vertex AI pipeline with parameters you want to track as arguments to your PipelineJob. Use the Metrics, Model, and Dataset artifact types from the Kubeflow Pipelines DSL as the inputs and outputs of the components in your pipeline.
2. Associate the pipeline with your experiment when you submit the job.

Correct Answer: B

Question 5

You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?

A. Use Cloud Scheduler to trigger a Cloud Function every week that runs the query for retraining the model.

B. Use the BigQuery API Connector and Cloud Scheduler to trigger Workflows every week that retrains the model.

C. Use BigQuery's scheduling service to run the model retraining query periodically.

D. Create a pipeline in Vertex AI Pipelines that executes the retraining query, and use the Cloud Scheduler API to run the query weekly.

Correct Answer: C

Question 6

You work at a leading healthcare firm developing state-of-the-art algorithms for various use cases. You have unstructured textual data with custom labels. You need to extract and classify various medical phrases with these labels. What should you do?

A. Use AutoML Entity Extraction to train a medical entity extraction model

B. Use the Healthcare Natural Language API to extract medical entities

C. Use TensorFlow to build a custom medical entity extraction model

D. Use a BERT-based model to fine-tune a medical entity extraction model

Correct Answer: A

Question 7

You are an ML engineer at a media company. You need to build an ML model to analyze video content, identify objects, and alert users if there is inappropriate content. Which Google Cloud products should you use to build this project?

A. Pub/Sub, Cloud Function, Video Intelligence API, Cloud Logging

B. Pub/Sub, Cloud Function, Cloud Vision API

C. Pub/Sub, Cloud Function, AutoML Video Intelligence, Cloud Logging

D. Pub/Sub, Cloud IoT, Dataflow, Cloud Vision API, Cloud Logging

Correct Answer: A

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Question 8

You are developing an ML model on Vertex AI that needs to meet specific interpretability requirements for regulatory compliance. You want to use a combination of model architectures and modeling techniques to maximize accuracy and interpretability. How should you create the model?

A. Use a boosted decision tree-based model architecture, and use SHAP values for interpretability.

B. Use a convolutional neural network (CNN)-based deep learning model architecture, and use local interpretable model-agnostic explanations (LIME) for interpretability.

C. Use a recurrent neural network (RNN)-based deep learning model architecture, and use integrated gradients for interpretability.

D. Use a long short-term memory (LSTM)-based model architecture, and use local interpretable model-agnostic explanations (LIME) for interpretability.

Correct Answer: A

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Question 9

You work for a company that is developing an application to help users with meal planning.
You want to use machine learning to scan a corpus of recipes and extract each ingredient (e.g., carrot, rice, pasta) and each kitchen cookware (e.g., bowl, pot, spoon) mentioned. Each recipe is saved in an unstructured text file. What should you do?

A. Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe. Evaluate the model's performance on a prelabeled dataset.

B. Create a multi-label text classification dataset on Vertex AI. Create a test dataset, and label each recipe that corresponds to its ingredients and cookware. Train a multi-class classification model. Evaluate the model's performance on a holdout dataset.

C. Create a text dataset on Vertex AI for entity extraction. Create two entities called ingredient and cookware and label at least 200 examples of each entity. Train an AutoML entity extraction model to extract occurrences of these entity types. Evaluate performance on a holdout dataset.

D. Create a text dataset on Vertex AI for entity extraction. Create as many entities as there are different ingredients and cookware. Train an AutoML entity extraction model to extract those entities. Evaluate the model's performance on a holdout dataset.

Correct Answer: C

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Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) Free Practice Test