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

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

• Question 171:

  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.
  B. Translate the normalization algorithm into SQL for use with BigQuery.

• Question 172:

  You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?

  A. Use the AI Platform Training built-in algorithms to create a custom model.
  B. Use AutoMlL Natural Language to extract custom entities for classification.
  C. Use the Cloud Natural Language API to extract custom entities for classification.
  D. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm.
  B. Use AutoMlL Natural Language to extract custom entities for classification.

• Question 173:

  You work for a retailer that sells clothes to customers around the world. You have been tasked with ensuring that ML models are built in a secure manner. Specifically, you need to protect sensitive customer data that might be used in the models. You have identified four fields containing sensitive data that are being used by your data science team: AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE. What should you do with the data before it is made available to the data science team for training purposes?

  A. Tokenize all of the fields using hashed dummy values to replace the real values.
  B. Use principal component analysis (PCA) to reduce the four sensitive fields to one PCA vector.
  C. Coarsen the data by putting AGE into quantiles and rounding LATITUDE_LONGTTUDE into single precision. The other two fields are already as coarse as possible.
  D. Remove all sensitive data fields, and ask the data science team to build their models using non-sensitive data.
  A. Tokenize all of the fields using hashed dummy values to replace the real values.

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  Explanation/Reference:

  https://cloud.google.com/blog/products/identity-security/take-charge-of-your-data-how-tokenization-makes-data-usable-without-sacrificing-privacy

• Question 174:

  You have trained a DNN regressor with TensorFlow to predict housing prices using a set of predictive features. Your default precision is tf.float64, and you use a standard TensorFlow estimator:

  

  Your model performs well, but just before deploying it to production, you discover that your current serving latency is 10ms @ 90 percentile and you currently serve on CPUs. Your production requirements expect a model latency of 8ms @ 90 percentile. You're willing to accept a small decrease in performance in order to reach the latency requirement.

  Therefore your plan is to improve latency while evaluating how much the model's prediction decreases. What should you first try to quickly lower the serving latency?

  A. Switch from CPU to GPU serving.
  B. Apply quantization to your SavedModel by reducing the floating point precision to tf.float16.
  C. Increase the dropout rate to 0.8 and retrain your model.
  D. Increase the dropout rate to 0.8 in _PREDICT mode by adjusting the TensorFlow Serving parameters.
  A. Switch from CPU to GPU serving.

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  Explanation/Reference:

  https://www.tensorflow.org/lite/performance/post_training_quantization#float16_quantization

• Question 175:

  You work for a large retailer, and you need to build a model to predict customer chum. The company has a dataset of historical customer data, including customer demographics purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?

  A. Create a linear regression model in BigQuery ML, and register the model in Vertex AI Model Registry. Evaluate the model performance in Vertex AI .
  B. Create a logistic regression model in BigQuery ML and register the model in Vertex AI Model Registry. Evaluate the model performance in Vertex AI .
  C. Create a linear regression model in BigQuery ML. Use the ML.EVALUATE function to evaluate the model performance.
  D. Create a logistic regression model in BigQuery ML. Use the ML.CONFUSION_MATRIX function to evaluate the model performance.
  B. Create a logistic regression model in BigQuery ML and register the model in Vertex AI Model Registry. Evaluate the model performance in Vertex AI .

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  Explanation/Reference:

• Question 176:

  You work as an analyst at a large banking firm. You are developing a robust scalable ML pipeline to tram several regression and classification models. Your primary focus for the pipeline is model interpretability. You want to productionize the pipeline as quickly as possible. What should you do?

  A. Use Tabular Workflow for Wide and Deep through Vertex AI Pipelines to jointly train wide linear models and deep neural networks
  B. Use Google Kubernetes Engine to build a custom training pipeline for XGBoost-based models
  C. Use Tabular Workflow for TabNet through Vertex AI Pipelines to train attention-based models
  D. Use Cloud Composer to build the training pipelines for custom deep learning-based models
  C. Use Tabular Workflow for TabNet through Vertex AI Pipelines to train attention-based models

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  Explanation/Reference:

• Question 177:

  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?

  A. 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.
  B. 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.
  C. 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.
  D. 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.
  B. 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.

• Question 178:

  You are building a linear model with over 100 input features, all with values between ? and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

  A. Use principal component analysis (PCA) to eliminate the least informative features.
  B. Use L1 regularization to reduce the coefficients of uninformative features to 0.
  C. After building your model, use Shapley values to determine which features are the most informative.
  D. Use an iterative dropout technique to identify which features do not degrade the model when removed.
  B. Use L1 regularization to reduce the coefficients of uninformative features to 0.

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  Explanation/Reference:

  https://www.quora.com/How-does-the-L1-regularization-method-help-in-feature-selection https://developers.google.com/machine-learning/crash-course/regularization-for-sparsity/l1-regularization

• Question 179:

  You work for a company that captures live video footage of checkout areas in their retail stores. You need to use the live video footage to build a model to detect the number of customers waiting for service in near real time. You want to implement a solution quickly and with minimal effort. How should you build the model?

  A. Use the Vertex AI Vision Occupancy Analytics model.
  B. Use the Vertex AI Vision Person/vehicle detector model.
  C. Train an AutoML object detection model on an annotated dataset by using Vertex AutoML.
  D. Train a Seq2Seq+ object detection model on an annotated dataset by using Vertex AutoML.
  A. Use the Vertex AI Vision Occupancy Analytics model.

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  Explanation/Reference:

• Question 180:

  You have trained a text classification model in TensorFlow using AI 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 AI Platform.
  C. Use Dataflow with the SavedModel to read the data from BigQuery.
  D. Submit a batch prediction job on AI Platform that points to the model location in Cloud Storage.
  D. Submit a batch prediction job on AI Platform that points to the model location in Cloud Storage.

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  Explanation/Reference:

  https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions