Free Google Professional-Machine-Learning-Engineer Exam Questions

Question 1

You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?

A : Store features in Bigtable as key/value data.

B : Store features in Vertex Al Feature Store.

C : Store features as a Vertex Al dataset and use those features to tram the models hosted in Vertex Al endpoints.

D : Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.

Answer: B

Question 2

You work for an organization that operates a streaming music service. You have a custom production model that is serving a "next song" recommendation based on a user’s recent listening history. Your model is deployed on a Vertex Al endpoint. You recently retrained the same model by using fresh data. The model received positive test results offline. You now want to test the new model in production while minimizing complexity. What should you do?

A : Create a new Vertex Al endpoint for the new model and deploy the new model to that new endpoint Build a service to randomly send 5% of production traffic to the new endpoint Monitor end-user metrics such as listening time If end-user metrics improve between models over time gradually increase the percentage of production traffic sent to the new endpoint.

B : Capture incoming prediction requests in BigQuery Create an experiment in Vertex Al Experiments Run batch predictions for both models using the captured data Use the user's selected song to compare the models performance side by side If the new models performance metrics are better than the previous model deploy the new model to production

C : Deploy the new model to the existing Vertex Al endpoint Use traffic splitting to send 5% of production traffic to the new model Monitor end-user metrics, such as listening time If end-user metrics improve between models over time, gradually increase the percentage of production traffic sent to the new model.

D : Configure a model monitoring job for the existing Vertex Al endpoint. Configure the monitoring job to detect prediction drift, and set a threshold for alerts Update the model on the endpoint from the previous model to the new model If you receive an alert of prediction drift, revert to the previous model

Answer: C

Question 3

You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week which makes it difficult to track the experiment runs. You want a simple approach to effectively track, visualize and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?

A : Set up Vertex Al Experiments to track metrics and parameters Configure Vertex Al TensorBoard for visualization

B : Set up a Cloud Function to write and save metrics files to a Cloud Storage Bucket Configure a Google Cloud VM to host TensorBoard locally for visualization.

C : Set up a Vertex Al Workbench notebook instance Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.

D : Set up a Cloud Function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization

Answer: A

Question 4

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 the Healthcare Natural Language API to extract medical entities.

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

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

D : Use TensorFlow to build a custom medical entity extraction model.

Answer: B

Question 5

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 : 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.

B : eate a new view with BigQuery that does not include a column with city information

C : Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5, and then use that number to represent the city in the model.

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

Answer: B