Practice Professional-Machine-Learning-Engineer Exam Online

Question 1

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: C

Question 2

You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?

A : Write your data in TFRecords.

B : Z-normalize all the numeric features.

C : Oversample the fraudulent transaction 10 times.

D : Use one-hot encoding on all categorical features.

Answer: C

Question 3

You trained a text classification model. You have the following SignatureDefs:

You started a TensorFlow-serving component server and tried to send an HTTP request to get a prediction using: headers = {"content-type": "application/json"} json_response = requests.post('http: //localhost:8501/v1/models/text_model:predict', data=data, headers=headers)

What is the correct way to write the predict request?

A : data = json.dumps({ג€signature_nameג€: ג€seving_defaultג€, ג€instancesג€ [['ab', 'bc', 'cd']]})

B : data = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b', 'c', 'd', 'e', 'f']]})

C : data = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b', 'c'], ['d', 'e', 'f']]})

D : data = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b'], ['c', 'd'], ['e', 'f']]})

Answer: D

Question 4

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 5

You have developed a BigQuery ML model that predicts customer churn and deployed the model to Vertex Al Endpoints. You want to automate the retraining of your model by using minimal additional code when model feature values change. You also want to minimize the number of times that your model is retrained to reduce training costs. What should you do?

A : 1. Enable request-response logging on Vertex Al Endpoints.

2 Schedule a TensorFlow Data Validation job to monitor prediction drift

3. Execute model retraining if there is significant distance between the distributions

B : 1. Enable request-response logging on Vertex Al Endpoints

2. Schedule a TensorFlow Data Validation job to monitor training/serving skew

3. Execute model retraining if there is significant distance between the distributions

C : 1 Create a Vertex Al Model Monitoring job configured to monitor prediction drift.

2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitonng alert is

detected.

3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery

D : 1. Create a Vertex Al Model Monitoring job configured to monitor training/serving skew

2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is

detected

3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery.

Answer: C

Name:	Professional Machine Learning Engineer
Exam Code:	Professional-Machine-Learning-Engineer
Certification:	Google Cloud Certified
Vendor:	Google
Total Questions:	312
Last Updated:	Sep 04, 2026

Google Professional-Machine-Learning-Engineer Answers