Free Google Professional-Machine-Learning-Engineer Exam Questions

Question 1

As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

A : the batch prediction functionality of AI Platform.

B : eate a serving pipeline in Compute Engine for prediction.

C : Cloud Functions for prediction each time a new data point is ingested

D : ploy the model on AI Platform and create a version of it for online inference.

Answer: D

Question 2

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

Question 3

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 drift3. Execute model retraining if there is significant distance between the distributions

B : 1. Enable request-response logging on Vertex Al Endpoints2. Schedule a TensorFlow Data Validation job to monitor training/serving skew3. 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 isdetected.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 skew2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert isdetected3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery.

Answer: C

Question 4

You work for a semiconductor manufacturing company. You need to create a real-time application that automates the quality control process High-definition images of each semiconductor are taken at the end of the assembly line in real time. The photos are uploaded to a Cloud Storage bucket along with tabular data that includes each semiconductor's batch number serial number dimensions, and weight You need to configure model training and serving while maximizing model accuracy. What should you do?

A : Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Deploy the model and configure Pub/Sub to publish a message when an image is categorized into the failing class.

B : Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Schedule a daily batch prediction job that publishes a Pub/Sub message when the job completes.

C : Convert the images into an embedding representation Import this data into BigQuery, and train a BigQuery. ML K-means clustenng model with two clusters Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing cluster.

D : Import the tabular data into BigQuery use Vertex Al Data Labeling Service to label the data and train an AutoML tabular classification model Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing class.

Answer: D

Question 5

You are an ML engineer at a global car manufacture. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

A : ee individual features: binned latitude, binned longitude, and one-hot encoded car type.

B : feature obtained as an element-wise product between latitude, longitude, and car type.

C : feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type.

D : o feature crosses as an element-wise product: the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type.

Answer: C