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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 51–55 (page: 12)

Updated On: 25-Oct-2026
Page 12 of 82
PDF with 402 Questions

You work for an online travel agency that also sells advertising placements on its website to other companies. You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor. You want to Implement the simplest solution. How should you configure the prediction pipeline?

  1. Embed the client on the website, and then deploy the model on AI Platform Prediction.
  2. Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.
  3. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user’s navigation context, and then deploy the model on AI Platform Prediction.
  4. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user’s navigation context, and then deploy the model on Google Kubernetes Engine.

Answer(s): C



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?

  1. Create a tf.data.Dataset.prefetch transformation.
  2. Convert the images to tf.Tensor objects, and then run Dataset.from_tensor_slices().
  3. Convert the images to tf.Tensor objects, and then run tf.data.Dataset.from_tensors().
  4. Convert the images into TFRecords, store the images in Cloud Storage, and then use the tf.data API to read the images for training.

Answer(s): D

Explanation:


Reference:

https://www.tensorflow.org/api_docs/python/tf/data/Dataset



You work for a large retailer, and you need to build a model to predict customer churn. 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?

  1. Create a linear regression model in BigQuery ML, and register the model in Vertex AI Model Registry. Evaluate the model performance in Vertex AI.
  2. Create a logistic regression model in BigQuery ML and register the model in Vertex AI Model Registry. Evaluate the model performance in Vertex AI.
  3. Create a linear regression model in BigQuery ML. Use the ML.EVALUATE function to evaluate the model performance.
  4. Create a logistic regression model in BigQuery ML. Use the ML.CONFUSION_MATRIX function to evaluate the model performance.

Answer(s): B



You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort.
What should you do?

  1. Store features in Bigtable as key/value data.
  2. Store features in Vertex AI Feature Store.
  3. Store features as a Vertex AI dataset, and use those features to train the models hosted in Vertex AI endpoints.
  4. Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.

Answer(s): B



You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent.
Which data transformation strategy would likely improve the performance of your classifier?

  1. Write your data in TFRecords.
  2. Z-normalize all the numeric features.
  3. Oversample the fraudulent transaction 10 times.
  4. Use one-hot encoding on all categorical features.

Answer(s): C

Explanation:


Reference:

https://towardsdatascience.com/how-to-build-a-machine-learning-model-to-identify-credit-card-frau d-in-5-stepsa-hands-on-modeling-5140b3bd19f1



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