Free Google Professional-Machine-Learning-Engineer Exam Questions

Question 1

You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?

A : ore your tf.logging data in BigQuery.

B : nage all relational entities in the Hive Metastore.

C : ore all ML metadata in Google Cloud’s operations suite.

D : nage your ML workflows with Vertex ML Metadata.

Answer: C

Question 2

You are using Keras and TensorFlow to develop a fraud detection model Records of customer transactions are stored in a large table in BigQuery. You need to preprocess these records in a cost-effective and efficient way before you use them to train the model. The trained model will be used to perform batch inference in BigQuery. How should you implement the preprocessing workflow?

A : Implement a preprocessing pipeline by using Apache Spark, and run the pipeline on Dataproc Save the preprocessed data as CSV files in a Cloud Storage bucket.

B : Load the data into a pandas DataFrame Implement the preprocessing steps using panda’s transformations. and train the model directly on the DataFrame.

C : Perform preprocessing in BigQuery by using SQL Use the BigQueryClient in TensorFlow to read the data directly from BigQuery.

D : Implement a preprocessing pipeline by using Apache Beam, and run the pipeline on Dataflow Save the preprocessed data as CSV files in a Cloud Storage bucket.

Answer: D

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

Your organization manages an online message board. A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive. Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?

A : Add synthetic training data where those phrases are used in non-toxic ways.

B : move the model and replace it with human moderation.

C : place your model with a different text classifier.

D : ise the threshold for comments to be considered toxic or harmful.

Answer: A

Question 5

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