Free Google Professional Machine Learning Engineer Exam Professional-Machine-Learning-Engineer Exam Practice Test

You work for an online retailer. Your company has a few thousand short lifecycle products. Your company has five years of sales data stored in BigQuery. You have been asked to build a model that will make monthly sales predictions for each product. You want to use a solution that can be implemented quickly with minimal effort. What should you do?

• Use Prophet on Vertex Al Training to build a custom model.
• Use Vertex Al Forecast to build a NN-based model.
• Use BigQuery ML to build a statistical AR1MA_PLUS model.
• Use TensorFlow on Vertex Al Training to build a custom model.

You are training a deep learning model for semantic image segmentation with reduced training time. While using a Deep Learning VM Image, you receive the following error: The resource 'projects/deeplearning-platforn/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80' was not found. What should you do?

• Ensure that you have GPU quota in the selected region.
• Ensure that the required GPU is available in the selected region.
• Ensure that you have preemptible GPU quota in the selected region.
• Ensure that the selected GPU has enough GPU memory for the workload.

You are an ML engineer at a bank that has a mobile application. Management has asked you to build an ML-based biometric authentication for the app that verifies a customer's identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases. Which learning strategy should you recommend to train and deploy this ML model?

• Differential privacy
• Federated learning
• MD5 to encrypt data
• Data Loss Prevention API

You work for a large retailer and you need to build a model to predict customer churn. The company has a dataset of historical customer data, including customer demographics, purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?

• Create a linear regression model in BigQuery ML and register the model in Vertex Al Model Registry Evaluate the model performance in Vertex Al.
• Create a logistic regression model in BigQuery ML and register the model in Vertex Al Model Registry. Evaluate the model performance in Vertex Al.
• Create a linear regression model in BigQuery ML Use the ml. evaluate function to evaluate the model performance.
• Create a logistic regression model in BigQuery ML Use the ml.confusion_matrix function to evaluate the model performance.

You are an ML engineer at a travel company. You have been researching customers’ travel behavior for many years, and you have deployed models that predict customers’ vacation patterns. You have observed that customers’ vacation destinations vary based on seasonality and holidays; however, these seasonal variations are similar across years. You want to quickly and easily store and compare the model versions and performance statistics across years. What should you do?

• Store the performance statistics in Cloud SQL. Query that database to compare the performance statistics across the model versions.
• Create versions of your models for each season per year in Vertex AI. Compare the performance statistics across the models in the Evaluate tab of the Vertex AI UI.
• Store the performance statistics of each pipeline run in Kubeflow under an experiment for each season per year. Compare the results across the experiments in the Kubeflow UI.
• Store the performance statistics of each version of your models using seasons and years as events in Vertex ML Metadata. Compare the results across the slices.

You developed a BigQuery ML linear regressor model by using a training dataset stored in a BigQuery table. New data is added to the table every minute. You are using Cloud Scheduler and Vertex Al Pipelines to automate hourly model training, and use the model for direct inference. The feature preprocessing logic includes quantile bucketization and MinMax scaling on data received in the last hour. You want to minimize storage and computational overhead. What should you do?

• Create a component in the Vertex Al Pipelines directed acyclic graph (DAG) to calculate the required statistics, and pass the statistics on to subsequent components.
• Preprocess and stage the data in BigQuery prior to feeding it to the model during training and inference.
• Create SQL queries to calculate and store the required statistics in separate BigQuery tables that are referenced in the CREATE MODEL statement.
• Use the TRANSFORM clause in the CREATE MODEL statement in the SQL query to calculate the required statistics.

Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises CPU-only infrastructure were encouraging, but have slow convergence. You have been asked to speed up model training to reduce time-to- market. You want to experiment with virtual machines (VMs) on Google Cloud to leverage more powerful hardware. Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. Which environment should you train your model on?

• AVM on Compute Engine and 1 TPU with all dependencies installed manually.
• AVM on Compute Engine and 8 GPUs with all dependencies installed manually.
• A Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed.
• A Deep Learning VM with more powerful CPU e2-highcpu-16 machines with all libraries pre- installed.

Your company manages an ecommerce website. You developed an ML model that recommends additional products to users in near real time based on items currently in the user's cart. The workflow will include the following processes.

• The website will send a Pub/Sub message with the relevant data and then receive a message with the prediction from Pub/Sub.
• Predictions will be stored in BigQuery
• 3. The model will be stored in a Cloud Storage bucket and will be updated frequently
• You want to minimize prediction latency and the effort required to update the model How should you reconfigure the architecture?
• Write a Cloud Function that loads the model into memory for prediction Configure the function to betriggered when messages are sent to Pub/Sub.
• Create a pipeline in Vertex Al Pipelines that performs preprocessing, prediction and postprocessingConfigure the pipeline to be triggered by a Cloud Function when messages are sent to Pub/Sub.
• Expose the model as a Vertex Al endpoint Write a custom DoFn in a Dataflow job that calls the endpoint forprediction.
• Use the Runlnference API with watchFilePatterr. in a Dataflow job that wraps around the model and serves predictions.

You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech- to-Text API. You want to minimize data preprocessing and development time. How should you build the model?

• Use the Al Platform Training built-in algorithms to create a custom model
• Use AutoML Natural Language to extract custom entities for classification
• Use the Cloud Natural Language API to extract custom entities for classification
• Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm

You have deployed a scikit-learn model to a Vertex Al endpoint using a custom model server. You enabled auto scaling; however, the deployed model fails to scale beyond one replica, which led to dropped requests. You notice that CPU utilization remains low even during periods of high load. What should you do?

• Attach a GPU to the prediction nodes.
• Increase the number of workers in your model server.
• Schedule scaling of the nodes to match expected demand.
• Increase the minReplicaCount in your DeployedModel configuration.