Free Practice Google Professional-Machine-Learning-Engineer Exam Questions 2025

Stay ahead with 100% Free Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Dumps Practice Questions

Page: 1 / 58

Total 289 Questions | Updated On: Feb 13, 2025

Add To Cart

Question 1

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

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

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

D : Two 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

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 training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?

A : Increase the instance memory to 512 GB and increase the batch size.

B : Replace the NVIDIA P100 GPU with a v3-32 TPU in the training job.

C : Enable early stopping in your Vertex AI Training job.

D : Use the tf.distribute.Strategy API and run a distributed training job.

Answer: C

Question 4

You recently designed and built a custom neural network that uses critical dependencies specific to your organization’s framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by AI Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

A : Use a built-in model available on AI Platform Training.

B : Build your custom container to run jobs on AI Platform Training.

C : Build your custom containers to run distributed training jobs on AI Platform Training.

D : Reconfigure your code to a ML framework with dependencies that are supported by AI Platform Training.

Answer: D

Question 5

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

Page: 1 / 58

Total 289 Questions | Updated On: Feb 13, 2025

Add To Cart