Google PROFESSIONAL-MACHINE-LEARNING-ENGINEER Online Practice Questions and Exam Preparation

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

• Question 201:

  You have trained a model by using data that was preprocessed in a batch Dataflow pipeline. Your use case requires real-time inference. You want to ensure that the data preprocessing logic is applied consistently between training and serving. What should you do?

  A. Perform data validation to ensure that the input data to the pipeline is the same format as the input data to the endpoint.
  B. Refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline. Use the same code in the endpoint.
  C. Refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline. Share this code with the end users of the endpoint.
  D. Batch the real-time requests by using a time window and then use the Dataflow pipeline to preprocess the batched requests. Send the preprocessed requests to the endpoint.
  B. Refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline. Use the same code in the endpoint.

• Question 202:

  You need to train a regression model based on a dataset containing 50,000 records that is stored in BigQuery. The data includes a total of 20 categorical and numerical features with a target variable that can include negative values. You need to minimize effort and training time while maximizing model performance. What approach should you take to train this regression model?

  A. Create a custom TensorFlow DNN model
  B. Use BQML XGBoost regression to train the model.
  C. Use AutoML Tables to train the model without early stopping.
  D. Use AutoML Tables to train the model with RMSLE as the optimization objective.
  B. Use BQML XGBoost regression to train the model.

• Question 203:

  You work on a growing team of more than 50 data scientists who all use AI Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

  A. Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.
  B. Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.
  C. Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.
  D. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using
  C. Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.

  ---

  Explanation/Reference:

  https://cloud.google.com/ai-platform/prediction/docs/resource-labels#overview_of_labels

• Question 204:

  You developed a Vertex AI ML pipeline that consists of preprocessing and training steps and each set of steps runs on a separate custom Docker image. Your organization uses GitHub and GitHub Actions as CI/CD to run unit and integration tests. You need to automate the model retraining workflow so that it can be initiated both manually and when a new version of the code is merged in the main branch. You want to minimize the steps required to build the workflow while also allowing for maximum flexibility. How should you configure the CI/CD workflow?

  A. Trigger a Cloud Build workflow to run tests, build custom Docker images, push the images to Artifact Registry, and launch the pipeline in Vertex AI Pipelines.
  B. Trigger GitHub Actions to run the tests, launch a job on Cloud Run to build custom Docker images, push the images to Artifact Registry, and launch the pipeline in Vertex AI Pipelines.
  C. Trigger GitHub Actions to run the tests, build custom Docker images, push the images to Artifact Registry, and launch the pipeline in Vertex AI Pipelines.
  D. Trigger GitHub Actions to run the tests, launch a Cloud Build workflow to build custom Docker images, push the images to Artifact Registry, and launch the pipeline in Vertex AI Pipelines.
  D. Trigger GitHub Actions to run the tests, launch a Cloud Build workflow to build custom Docker images, push the images to Artifact Registry, and launch the pipeline in Vertex AI Pipelines.

• Question 205:

  You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?

  A. Retrain the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint,
  B. Retrain the model by using Vertex Al Deploy the model from Vertex AI Model. Registry to a Vertex AI endpoint.
  C. Alter the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint.
  D. Export the model from BigQuery ML to Cloud Storage. Import the model into Vertex AI Model Registry. Deploy the model to a Vertex AI endpoint.
  C. Alter the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint.

• Question 206:

  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
  B. Lack of model retraining

  ---

  Explanation/Reference:

  Retraining is needed as the market is changing.

• Question 207:

  You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network. What should you do?

  A. Create a hot-encoding of words, and feed the encodings into your model.
  B. Identify word embeddings from a pre-trained model, and use the embeddings in your model.
  C. Sort the words by frequency of occurrence, and use the frequencies as the encodings in your model.
  D. Assign a numerical value to each word from 1 to 100,000 and feed the values as inputs in your model.
  B. Identify word embeddings from a pre-trained model, and use the embeddings in your model.

  ---

  Explanation/Reference:

  https://developers.google.com/machine-learning/guides/text-classification/step-3 https://developers.google.com/machine-learning/guides/text-classification/step-4

• Question 208:

  You are developing a custom image classification model in Python. You plan to run your training application on Vertex Al Your input dataset contains several hundred thousand small images You need to determine how to store and access the images for training. You want to maximize data throughput and minimize training time while reducing the amount of additional code. What should you do?

  A. Store image files in Cloud Storage and access them directly.
  B. Store image files in Cloud Storage and access them by using serialized records.
  C. Store image files in Cloud Filestore, and access them by using serialized records.
  D. Store image files in Cloud Filestore and access them directly by using an NFS mount point.
  B. Store image files in Cloud Storage and access them by using serialized records.

• Question 209:

  Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendation model that will suggest articles to readers that are similar to the articles they are currently reading. Which approach should you use?

  A. Create a collaborative filtering system that recommends articles to a user based on the user's past behavior.
  B. Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity.
  C. Build a logistic regression model for each user that predicts whether an article should be recommended to a user.
  D. Manually label a few hundred articles, and then train an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories.
  B. Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity.

  ---

  Explanation/Reference:

  https://cloud.google.com/blog/topics/developers-practitioners/meet-ais-multitool-vector-embeddings

• Question 210:

  You are using Keras and TensorFlow to develop a fraud detection model. Records of customer transactions are stored in a large table in BigQuery. You need to preprocess these records in a cost-effective and efficient way before you use them to train the model. The trained model will be used to perform batch inference in BigQuery. How should you implement the preprocessing workflow?

  A. Implement a preprocessing pipeline by using Apache Spark, and run the pipeline on Dataproc. Save the preprocessed data as CSV files in a Cloud Storage bucket.
  B. Load the data into a pandas DataFrame. Implement the preprocessing steps using pandas transformations, and train the model directly on the DataFrame.
  C. Perform preprocessing in BigQuery by using SQL. Use the BigQueryClient in TensorFlow to read the data directly from BigQuery.
  D. Implement a preprocessing pipeline by using Apache Beam, and run the pipeline on Dataflow. Save the preprocessed data as CSV files in a Cloud Storage bucket.
  C. Perform preprocessing in BigQuery by using SQL. Use the BigQueryClient in TensorFlow to read the data directly from BigQuery.