Free Google Professional-Machine-Learning-Engineer Exam Questions

Question 1

You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

A : Use Principal Component Analysis to eliminate the least informative features.

B : Use L1 regularization to reduce the coefficients of uninformative features to 0.

C : After building your model, use Shapley values to determine which features are the most informative.

D : Use an iterative dropout technique to identify which features do not degrade the model when removed.

Answer: B

Question 2

You are collaborating on a model prototype with your team. You need to create a Vertex Al Workbench environment for the members of your team and also limit access to other employees in your project. What should you do?

A : 1. Create a new service account and grant it the Notebook Viewer role.2 Grant the Service Account User role to each team member on the service account.3 Grant the Vertex Al User role to each team member.4. Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.

B : 1. Grant the Vertex Al User role to the default Compute Engine service account.2. Grant the Service Account User role to each team member on the default Compute Engine serviceaccount.3. Provision a Vertex Al Workbench user-managed notebook instance that uses the default ComputeEngine service account.

C : 1 Create a new service account and grant it the Vertex Al User role.2 Grant the Service Account User role to each team member on the service account.3. Grant the Notebook Viewer role to each team member.4 Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.

D : 1 Grant the Vertex Al User role to the primary team member.2. Grant the Notebook Viewer role to the other team members.3. Provision a Vertex Al Workbench user-managed notebook instance that uses the primary usersaccount.

Answer: C

Question 3

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.MULTI_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.

Answer: D

Question 4

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 Cloud Spanner 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 a Dataflow pipeline to create the features, and store them in Vertex AI Feature Store 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 BigQuery for online prediction.

D : 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.

Answer: C

Question 5

You are building an ML model to detect anomalies in real-time sensor data. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?

A : 1 = Dataflow, 2 = AI Platform, 3 = BigQuery

B : 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable

C : 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions

D : 1 = BigQuery, 2 = AI Platform, 3 = Cloud Storage

Answer: C