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

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

• Question 131:

  You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution. What should you do?

  A. Use feature construction to combine the strongest features.
  B. Use the representation transformation (normalization) technique.
  C. Improve the data cleaning step by removing features with missing values.
  D. Change the partitioning step to reduce the dimension of the test set and have a larger training set.
  B. Use the representation transformation (normalization) technique.

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  Explanation/Reference:

  https://developers.google.com/machine-learning/data-prep/transform/transform-numeric

• Question 132:

  You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

  A. Build a classification model
  B. Build a knowledge-based filtering model
  C. Build a collaborative-based filtering model
  D. Build a regression model using the features as predictors
  C. Build a collaborative-based filtering model

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  Explanation/Reference:

  Reference: https://cloud.google.com/solutions/recommendations-using-machine-learning-on-compute-engine

• Question 133:

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

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  Explanation/Reference:

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

• Question 134:

  You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine part failed. You recently started experimenting with a few different preprocessing and modeling approaches in a Vertex AI Workbench notebook. You want to log data and track artifacts from each run. How should you set up your experiments?

  A. 1. Use the Vertex AI SDK to create an experiment and set up Vertex ML Metadata. 2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_merrics function to log loss values.
  B. 1. Use the Vertex AI SDK to create an experiment and set up Vertex ML Metadata. 2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_metrics function to log loss values.
  C. 1. Create a Vertex AI TensorBoard instance and use the Vertex AI SDK to create an experiment and associate the TensorBoard instance. 2. Use the assign_input_artifact method to track the preprocessed data and use the log_time_series_metrics function to log loss values.
  D. 1. Create a Vertex AI TensorBoard instance, and use the Vertex AI SDK to create an experiment and associate the TensorBoard instance. 2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_metrics function to log loss values.
  B. 1. Use the Vertex AI SDK to create an experiment and set up Vertex ML Metadata. 2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_metrics function to log loss values.

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  Explanation/Reference:

• Question 135:

  You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured data. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?

  A. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Vertex AI Pipelines.
  B. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.
  C. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
  D. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
  B. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.

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  Explanation/Reference:

  https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline#sdk

• Question 136:

  You work for a social media company. You need to detect whether posted images contain cars. Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to AI Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the AI Platform Prediction model version. You notice that the precision is lower than your business requirements allow. How should you adjust the model's final layer softmax threshold to increase precision?

  A. Increase the recall.
  B. Decrease the recall.
  C. Increase the number of false positives.
  D. Decrease the number of false negatives.
  B. Decrease the recall.

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  Explanation/Reference:

  https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall

• Question 137:

  You need to build an ML model for a social media application to predict whether a user's submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements. How should you build a model to ensure that the application does not falsely accept a non-compliant picture?

  A. Use AutoML to optimize the model's recall in order to minimize false negatives.
  B. Use AutoML to optimize the model's F1 score in order to balance the accuracy of false positives and false negatives.
  C. Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that meet the profile photo requirements.
  D. Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that do not meet the profile photo requirements.
  B. Use AutoML to optimize the model's F1 score in order to balance the accuracy of false positives and false negatives.

• Question 138:

  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.
  A. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.

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  Explanation/Reference:

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

• Question 139:

  You recently developed a wide and deep model in TensorFlow. You generated training datasets using a SQL script that preprocessed raw data in BigQuery by performing instance-level transformations of the data. You need to create a training pipeline to retrain the model on a weekly basis. The trained model will be used to generate daily recommendations. You want to minimize model development and training time. How should you develop the training pipeline?

  A. Use the Kubeflow Pipelines SDK to implement the pipeline. Use the BigQueryJobOp component to run the preprocessing script and the CustomTrainingJobOp component to launch a Vertex AI training job.
  B. Use the Kubeflow Pipelines SDK to implement the pipeline. Use the DataflowPythonJobOp component to preprocess the data and the CustomTrainingJobOp component to launch a Vertex AI training job.
  C. Use the TensorFlow Extended SDK to implement the pipeline Use the ExampleGen component with the BigQuery executor to ingest the data the Transform component to preprocess the data, and the Trainer component to launch a Vertex AI training job.
  D. Use the TensorFlow Extended SDK to implement the pipeline Implement the preprocessing steps as part of the input_fn of the model. Use the ExampleGen component with the BigQuery executor to ingest the data and the Trainer component to launch a Vertex AI training job.
  A. Use the Kubeflow Pipelines SDK to implement the pipeline. Use the BigQueryJobOp component to run the preprocessing script and the CustomTrainingJobOp component to launch a Vertex AI training job.

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  Explanation/Reference:

• Question 140:

  You need to use TensorFlow to train an image classification model. Your dataset is located in a Cloud Storage directory and contains millions of labeled images. Before training the model, you need to prepare the data. You want the data preprocessing and model training workflow to be as efficient, scalable, and low maintenance as possible. What should you do?

  A. 1. Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory. 2. Reference tf.data.TFRecordDataset in the training script. 3. Train the model by using Vertex AI Training with a V100 GPU.
  B. 1. Create a Dataflow job that moves the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label 2. Reference tfds.folder_dataset:ImageFolder in the training script. 3. Train the model by using Vertex AI Training with a V100 GPU.
  C. 1. Create a Jupyter notebook that uses an nt-standard-64 V100 GPU Vertex AI Workbench instance. 2. Write a Python script that creates sharded TFRecord files in a directory inside the instance. 3. Reference tf.data.TFRecordDataset in the training script. 4. Train the model by using the Workbench instance.
  D. 1. Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex AI Workbench instance. 2. Write a Python script that copies the images into multiple Cloud Storage directories, where each. directory is named according to the corresponding label. 3. Reference tfds.foladr_dataset.ImageFolder in the training script. 4. Train the model by using the Workbench instance.
  A. 1. Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory. 2. Reference tf.data.TFRecordDataset in the training script. 3. Train the model by using Vertex AI Training with a V100 GPU.