Professional Machine Learning Engineer Practice Test

Professional Machine Learning Engineer Practice Test practice questions, page 10 of 15, with answer review and explanations.

Question 46

As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

  1. Use the batch prediction functionality of AI Platform.
  2. Create a serving pipeline in Compute Engine for prediction.
  3. Use Cloud Functions for prediction each time a new data point is ingested.
  4. Deploy the model on AI Platform and create a version of it for online inference.

Question 47

You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

  1. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.
  2. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
  3. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.
  4. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.

Question 48

You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

  1. Address the model overfitting by using a less complex algorithm.
  2. Address data leakage by applying nested cross-validation during model training.
  3. Address data leakage by removing features highly correlated with the target value.
  4. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

Question 49

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.

Question 50

Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises CPU-only infrastructure were encouraging, but have slow convergence. You have been asked to speed up model training to reduce time-to-market. You want to experiment with virtual machines (VMs) on Google Cloud to leverage more powerful hardware. Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. Which environment should you train your model on?

  1. AVM on Compute Engine and 1 TPU with all dependencies installed manually.
  2. AVM on Compute Engine and 8 GPUs with all dependencies installed manually.
  3. A Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed.
  4. A Deep Learning VM with more powerful CPU e2-highcpu-16 machines with all libraries pre-installed.