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Professional-Machine-Learning-Engineer Exam Dumps - Google Machine Learning Engineer Questions and Answers

Question # 4

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

Options:

A.

Compare the loss performance for each model on a held-out dataset.

B.

Compare the loss performance for each model on the validation data

C.

Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool

D.

Compare the mean average precision across the models using the Continuous Evaluation feature

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Question # 5

You are building a MLOps platform to automate your company's ML experiments and model retraining. You need to organize the artifacts for dozens of pipelines How should you store the pipelines' artifacts'?

Options:

A.

Store parameters in Cloud SQL and store the models' source code and binaries in GitHub

B.

Store parameters in Cloud SQL store the models' source code in GitHub, and store the models' binaries in Cloud Storage.

C.

Store parameters in Vertex ML Metadata store the models' source code in GitHub and store the models' binaries in Cloud Storage.

D.

Store parameters in Vertex ML Metadata and store the models source code and binaries in GitHub.

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Question # 6

You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results. What should you do?

Options:

A.

Extract sentiment directly from the voice recordings

B.

Convert the speech to text and build a model based on the words

C.

Convert the speech to text and extract sentiments based on the sentences

D.

Convert the speech to text and extract sentiment using syntactical analysis

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Question # 7

You work on the data science team at a manufacturing company. You are reviewing the company's historical sales data, which has hundreds of millions of records. For your exploratory data analysis, you need to calculate descriptive statistics such as mean, median, and mode; conduct complex statistical tests for hypothesis testing; and plot variations of the features over time You want to use as much of the sales data as possible in your analyses while minimizing computational resources. What should you do?

Options:

A.

Spin up a Vertex Al Workbench user-managed notebooks instance and import the dataset Use this data to create statistical and visual analyses

B.

Visualize the time plots in Google Data Studio. Import the dataset into Vertex Al Workbench user-managed notebooks Use this data to calculate the descriptive statistics and run the statistical analyses

C.

Use BigQuery to calculate the descriptive statistics. Use Vertex Al Workbench user-managed notebooks to visualize the time plots and run the statistical analyses.

D Use BigQuery to calculate the descriptive statistics, and use Google Data Studio to visualize the time plots. Use Vertex Al Workbench user-managed notebooks to run the statistical analyses.

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Question # 8

You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?

Options:

A.

Normalize the data using Google Kubernetes Engine

B.

Translate the normalization algorithm into SQL for use with BigQuery

C.

Use the normalizer_fn argument in TensorFlow's Feature Column API

D.

Normalize the data with Apache Spark using the Dataproc connector for BigQuery

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Question # 9

Your data science team is training a PyTorch model for image classification based on a pre-trained RestNet model. You need to perform hyperparameter tuning to optimize for several parameters. What should you do?

Options:

A.

Convert the model to a Keras model, and run a Keras Tuner job.

B.

Run a hyperparameter tuning job on AI Platform using custom containers.

C.

Create a Kuberflow Pipelines instance, and run a hyperparameter tuning job on Katib.

D.

Convert the model to a TensorFlow model, and run a hyperparameter tuning job on AI Platform.

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Question # 10

You work for a company that is developing a new video streaming platform. You have been asked to create a recommendation system that will suggest the next video for a user to watch. After a review by an AI Ethics team, you are approved to start development. Each video asset in your company’s catalog has useful metadata (e.g., content type, release date, country), but you do not have any historical user event data. How should you build the recommendation system for the first version of the product?

Options:

A.

Launch the product without machine learning. Present videos to users alphabetically, and start collecting user event data so you can develop a recommender model in the future.

B.

Launch the product without machine learning. Use simple heuristics based on content metadata to recommend similar videos to users, and start collecting user event data so you can develop a recommender model in the future.

C.

Launch the product with machine learning. Use a publicly available dataset such as MovieLens to train a model using the Recommendations AI, and then apply this trained model to your data.

D.

Launch the product with machine learning. Generate embeddings for each video by training an autoencoder on the content metadata using TensorFlow. Cluster content based on the similarity of these embeddings, and then recommend videos from the same cluster.

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Question # 11

Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?

Options:

A.

1 = Dataflow, 2 = BigQuery

B.

1 = Pub/Sub, 2 = Datastore

C.

1 = Dataflow, 2 = Cloud SQL

D.

1 = Cloud Function, 2 = Cloud SQL

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Question # 12

You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?

Options:

A.

Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.

B.

Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery

C.

Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler

D.

Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model

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Question # 13

You are an ML engineer at a mobile gaming company. A data scientist on your team recently trained a TensorFlow model, and you are responsible for deploying this model into a mobile application. You discover that the inference latency of the current model doesn’t meet production requirements. You need to reduce the inference time by 50%, and you are willing to accept a small decrease in model accuracy in order to reach the latency requirement. Without training a new model, which model optimization technique for reducing latency should you try first?

Options:

A.

Weight pruning

B.

Dynamic range quantization

C.

Model distillation

D.

Dimensionality reduction

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Exam Name: Google Professional Machine Learning Engineer
Last Update: Mar 31, 2025
Questions: 285
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