Professional-Machine-Learning-Engineer Exam Dumps – Our Features

You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?

• Use Vertex AI Workbench user-managed notebooks to generate the report.
• Use the Google Data Studio to create the report.
• Use the output from TensorFlow Data Validation on Dataflow to generate the report.
• Use Dataprep to create the report.

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?

• 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.
• Ingest the Avro files into BigQuery to perform analytics Use a Dataflow pipeline to create the features, and store them in Vertex Al Feature Store for online prediction.
• 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.
• Ingest the Avro files into Cloud Spanner to perform analytics. Use a Dataflow pipeline to create the features. and store them in Vertex Al Feature Store for online prediction.

You recently trained an XGBoost model on tabular data You plan to expose the model for internal use as an HTTP microservice After deployment you expect a small number of incoming requests. You want to productionize the model with the least amount of effort and latency. What should you do?

• Deploy the model to BigQuery ML by using CREATE model with the BOOSTED-THREE-REGRESSOR statement and invoke the BigQuery API from the microservice.
• Build a Flask-based app Package the app in a custom container on Vertex Al and deploy it to Vertex Al Endpoints.
• Build a Flask-based app Package the app in a Docker image and deploy it to Google Kubernetes Engine in Autopilot mode.
• Use a prebuilt XGBoost Vertex container to create a model and deploy it to Vertex Al Endpoints.

Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

• 1. Create a Pub/Sub topic for each user
• 2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
• Create a Pub/Sub topic for each user
• Deploy an application on the App Engine standard environment that sends a notification when your model predicts that
• a user's account balance will drop below the $25 threshold C.
• Build a notification system on Firebase
• Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold
• 1 Build a notification system on Firebase
• 2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold

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?

• Store features in Bigtable as key/value data.
• Store features in Vertex Al Feature Store.
• Store features as a Vertex Al dataset and use those features to tram the models hosted in Vertex Al endpoints.
• Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.

You are an ML engineer at a manufacturing company You are creating a classification model for apredictive maintenance use case You need to predict whether a crucial machine will fail in the next three days so that the repair crew has enough time to fix the machine before it breaks. Regular maintenance of the machine is relatively inexpensive, but a failure would be very costly You have trained several binary classifiers to predict whether the machine will fail. where a prediction of 1 means that the ML model predicts a failure.You are now evaluating each model on an evaluation dataset. You want to choose a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure. Which model should you choose?

• The model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0 5
• The model with the lowest root mean squared error (RMSE) and recall greater than 0.5.
• The model with the highest recall where precision is greater than 0.5.
• The model with the highest precision where recall is greater than 0.5.

You work for a hotel and have a dataset that contains customers' written comments scanned from paper-based customer feedback forms which are stored as PDF files Every form has the same layout. You need to quickly predict an overall satisfaction score from the customer comments on each form. How should you accomplish this task'?

• Use the Vision API to parse the text from each PDF file Use the Natural Language API analyzesentiment feature to infer overall satisfaction scores.
• Use the Vision API to parse the text from each PDF file Use the Natural Language API analyzeEntitysentiment feature to infer overall satisfaction scores.
• Uptrain a Document Al custom extractor to parse the text in the comments section of each PDF file. Use the Natural Language API analyze sentiment feature to infer overall satisfaction scores.
• Uptrain a Document Al custom extractor to parse the text in the comments section of each PDF file. Use the Natural Language API analyzeEntitySentiment feature to infer overall satisfaction scores.

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?

• Use the batch prediction functionality of Al Platform
• Create a serving pipeline in Compute Engine for prediction
• Use Cloud Functions for prediction each time a new data point is ingested
• Deploy the model on Al Platform and create a version of it for online inference.

You are developing a process for training and running your custom model in production. You need to be able to show lineage for your model and predictions. What should you do?

• 1 Create a Vertex Al managed dataset
• Use a Vertex Ai training pipeline to train your model
• Generate batch predictions in Vertex Al
• 1 Use a Vertex Al Pipelines custom training job component to train your model2. Generate predictions by using a Vertex Al Pipelines model batch predict component
• 1 Upload your dataset to BigQuery2. Use a Vertex Al custom training job to train your model
• Generate predictions by using Vertex Al SDK custom prediction routines
• 1 Use Vertex Al Experiments to train your model. 2 Register your model in Vertex Al Model Registry
• 3. Generate batch predictions in Vertex Al

You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?

• Create a Vertex Al Workbench user-managed notebook using the default VM instance, and use the%%bigquery magic commands in Jupyter to query the tables.
• Create a Vertex Al Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.
• Create a Vertex Al Workbench user-managed notebook on a Dataproc Hub. and use the%%bigquery magic commands in Jupyter to query the tables.
• Create a Vertex Al Workbench managed notebook on a Dataproc cluster, and use the spark- bigquery-connector to access the tables.