Google Google Certifications PROFESSIONAL-MACHINE-LEARNING-ENGINEER Questions & Answers

• Question 141:

  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 20 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. AIlow AutoML to interpret this array appropriately. Choose an automatic data split across the training, validation, and testing sets.

  B. Submit the data for training without performing any manual transformations. AIlow AutoML to handle the appropriate transformations. 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. AIlow 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 sets from 30 days after your validation set.

  Correct Answer: D

• Question 142:

  You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation data. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

  A. Apply a dropout parameter of 0.2, and decrease the learning rate by a factor of 10.

  B. Apply a L2 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.

  C. Run a hyperparameter tuning job on AI Platform to optimize for the L2 regularization and dropout parameters.

  D. Run a hyperparameter tuning job on AI Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.

  Correct Answer: C

  https://machinelearningmastery.com/introduction-to-regularization-to-reduce-overfitting-and-improve-generalization-error/

• Question 143:

  You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?

  A. Poor data quality

  B. Lack of model retraining

  C. Too few layers in the model for capturing information

  D. Incorrect data split ratio during model training, evaluation, validation, and test

  Correct Answer: B

  Retraining is needed as the market is changing.

• Question 144:

  You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?

  A. Create a tf.data.Dataset.prefetch transformation.

  B. Convert the images to tf.Tensor objects, and then run Dataset.from_tensor_slices().

  C. Convert the images to tf.Tensor objects, and then run tf.data.Dataset.from_tensors().

  D. Convert the images into TFRecords, store the images in Cloud Storage, and then use the tf.data API to read the images for training.

  Correct Answer: D

  Cite from Google Pag: to construct a Dataset from data in memory, use tf.data.Dataset.from_tensors() or tf.data.Dataset.from_tensor_slices(). When input data is stored in a file (not in memory), the recommended TFRecord format, you can use tf.data.TFRecordDataset().

  tf.data.Dataset is for data in memory. tf.data.TFRecordDataset is for data in non-memory storage.

• Question 145:

  You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations AI to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?

  A. Use the "Other Products You May Like" recommendation type to increase the click-through rate.

  B. Use the "Frequently Bought Together" recommendation type to increase the shopping cart size for each order.

  C. Import your user events and then your product catalog to make sure you have the highest quality event stream.

  D. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.

  Correct Answer: B

  Frequently bought together' recommendations aim to up-sell and cross-sell customers by providing product.

  Reference: https://rejoiner.com/resources/amazon-recommendations-secret-selling-online/

• Question 146:

  You are designing an architecture with a serverless 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 = AI Platform, 2 = AI Platform, 3 = AutoML Vision

  B. 1 = AI Platform, 2 = AI Platform, 3 = AutoML Natural Language

  C. 1 = AI Platform, 2 = AI Platform, 3 = Cloud Natural Language API

  D. 1 = Cloud Natural Language API, 2 = AI Platform, 3 = Cloud Vision API

  Correct Answer: C

  https://cloud.google.com/architecture/architecture-of-a-serverless-ml-model#architecture

  The architecture has the following flow:

  A user writes a ticket to Firebase, which triggers a Cloud Function.

  -The Cloud Function calls 3 different endpoints to enrich the ticket:

  -An AI Platform endpoint, where the function can predict the priority.

  -An AI Platform endpoint, where the function can predict the resolution time.

  -The Natural Language API to do sentiment analysis and word salience.

  -For each reply, the Cloud Function updates the Firebase real-time database.

  -The Cloud Function then creates a ticket into the helpdesk platform using the RESTful API.

• Question 147:

  You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving.

  What should you do?

  A. Configure AutoML Tables to perform the classification task.

  B. Run a BigQuery ML task to perform logistic regression for the classification.

  C. Use AI Platform Notebooks to run the classification model with pandas library.

  D. Use AI Platform to run the classification model job configured for hyperparameter tuning.

  Correct Answer: A

• Question 148:

  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.

  Correct Answer: A

  https://www.kubeflow.org/docs/components/pipelines/overview/pipelines-overview/) and deploy models

• Question 149:

  You are developing ML models with AI 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 AI 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.

  Correct Answer: C

  At the heart of this architecture is Cloud Build, infrastructure. Cloud Build can import source from Cloud Source Repositories, GitHub, or Bitbucket, and then execute a build to your specifications, and produce artifacts such as Docker containers or Python tar files.

• Question 150:

  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: [`drivers_license', `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

  Correct Answer: C

  Use sparse categorical crossentropy when your classes are mutually exclusive (e.g. when each sample belongs exactly to one class) and categorical crossentropy when one sample can have multiple classes or labels are soft probabilities (like [0.5, 0.3, 0.2]).