Free Google Professional-Machine-Learning-Engineer Exam Questions

Become Google Certified with updated Professional-Machine-Learning-Engineer exam questions and correct answers

Page: 1 / 58

Total 289 Questions | Updated On: Nov 12, 2024

Add To Cart

Question 1

You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100 000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training and are stored in a Cloud Storage bucket You need to be able to tune the model during each training run. How should you train the model?

A : Train a model for object detection by using Vertex Al AutoML.

B : Train a model for image classification by using Vertex Al AutoML.

C : Develop the model training code for object detection and tram a model by using Vertex Al custom training.

D : Develop the model training code for image classification and train a model by using Vertex Al custom training

Answer: C

Question 2

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

Question 3

You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?

A : Store features in Bigtable as key/value data.

B : Store features in Vertex Al Feature Store.

C : Store features as a Vertex Al dataset and use those features to tram the models hosted in Vertex Al endpoints.

D : Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.

Answer: B

Question 4

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 5

You have deployed multiple versions of an image classification model on AI Platform. You want to monitor the performance of the model versions over time. How should you perform this comparison?

A : mpare the loss performance for each model on a held-out dataset.

B : mpare the loss performance for each model on the validation data.

C : mpare the receiver operating characteristic (ROC) curve for each model using the What-If Tool.

D : mpare the mean average precision across the models using the Continuous Evaluation feature.

Answer: B

Page: 1 / 58

Total 289 Questions | Updated On: Nov 12, 2024

Add To Cart