Renovate Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Free Draindumps

NEW QUESTION 1
Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data. How should you address the input differences in production?

• A. Create alerts to monitor for skew, and retrain the model.
• B. Perform feature selection on the model, and retrain the model with fewer features
• C. Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service
• D. Perform feature selection on the model, and retrain the model on a monthly basis with fewer features

Answer: C

NEW QUESTION 2
You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set. What should you do?

• A. Keep the original test dataset unchanged even if newer products are incorporated into retraining
• B. Extend your test dataset with images of the newer products when they are introduced to retraining
• C. Replace your test dataset with images of the newer products when they are introduced to retraining.
• D. Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.

Answer: C

NEW QUESTION 3
You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?

• A. Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.
• B. Write a Python script that uses the BigQuery API to execute queries against BigQuery Execute this script as the first step in your Kubeflow pipeline
• C. Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries
• D. Locate the Kubeflow Pipelines repository on GitHub Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipelin
• E. Use the component to execute queries against BigQuery

Answer: A

NEW QUESTION 4
Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?

• A. Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
• B. Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.
• C. Use Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
• D. Use Al Platform Notebooks to execute the experiment
• E. Collect the results in a shared Google Sheetsfile, and query the results using the Google Sheets API

Answer: A

NEW QUESTION 5
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?

• 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

Answer: B

NEW QUESTION 6
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

• A. Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriatelyChoose an automatic data split across the training, validation, and testing sets
• B. Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriatetransformations Choose an automatic data split across the training, validation, and testing sets
• C. Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
• D. Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set

Answer: D

NEW QUESTION 7
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

• A. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
• B. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
• C. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
• D. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.

Answer: A

NEW QUESTION 8
You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:

You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?

• A. Modify the 'epochs' parameter
• B. Modify the 'scale-tier' parameter
• C. Modify the batch size' parameter
• D. Modify the 'learning rate' parameter

Answer: A

NEW QUESTION 9
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?

• A. Categorical hinge
• B. Binary cross-entropy
• C. Categorical cross-entropy
• D. Sparse categorical cross-entropy

Answer: B

NEW QUESTION 10
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?

• A. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
• B. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
• C. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucke
• D. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
• E. Use Cloud Scheduler to schedule jobs at a regular interva
• F. For the first step of the jo
• G. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.

Answer: A

NEW QUESTION 11
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?

• A. • Validate the accuracy of the model that you trained on preprocessed data• Create a new model that uses the raw data and is available in real time• Deploy the new model onto Al Platform for online prediction
• B. • Send incoming prediction requests to a Pub/Sub topic• Transform the incoming data using a Dataflow job• Submit a prediction request to Al Platform using the transformed data• Write the predictions to an outbound Pub/Sub queue
• C. • Stream incoming prediction request data into Cloud Spanner• Create a view to abstract your preprocessing logic.• Query the view every second for new records• Submit a prediction request to Al Platform using the transformed data• Write the predictions to an outbound Pub/Sub queue.
• D. • Send incoming prediction requests to a Pub/Sub topic• Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.• Implement your preprocessing logic in the Cloud Function• Submit a prediction request to Al Platform using the transformed data• Write the predictions to an outbound Pub/Sub queue

Answer: D

NEW QUESTION 12
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

• A. Export the model to BigQuery ML.
• B. Deploy and version the model on Al Platform.
• C. Use Dataflow with the SavedModel to read the data from BigQuery
• D. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.

Answer: A

NEW QUESTION 13
You are designing an architecture with a serveress ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:

Which endpoints should the Enrichment Cloud Functions call?

• A. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Vision
• B. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Natural Language
• C. 1 = Al Platform, 2 = Al Platform, 3 = Cloud Natural Language API
• D. 1 = cloud Natural Language API, 2 = Al Platform, 3 = Cloud Vision API

Answer: B

NEW QUESTION 14
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?

• A. 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.
• B. 1. Create a Pub/Sub topic for each user* 2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts thata user's account balance will drop below the $25 threshold
• C. 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 the average of all account balance predictions drops below the $25 threshold
• D. 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

Answer: B

NEW QUESTION 15
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?

• 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

Answer: B

NEW QUESTION 16
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

• A. Ensure that training is reproducible
• B. Ensure that all hyperparameters are tuned
• C. Ensure that model performance is monitored
• D. Ensure that feature expectations are captured in the schema

Answer: B

NEW QUESTION 17
You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?

• A. AutoML Vision model
• B. AutoML Vision Edge mobile-versatile-1 model
• C. AutoML Vision Edge mobile-low-latency-1 model
• D. AutoML Vision Edge mobile-high-accuracy-1 model

Answer: A

NEW QUESTION 18
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