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Google Professional Machine Learning Engineer Exam

Certification Provider: Google
Exam Name: Google Professional Machine Learning Engineer
Number of questions in our database: 146
Exam Version: Jan. 24, 2023
Exam Official Topics:
  • Topic 1: Performance and business quality of ML model predictions/ Establishing continuous evaluation metrics
  • Topic 2: Design architecture that complies with regulatory and security concerns/ Define business success criteria
  • Topic 3: Automation of data preparation and model training/deployment/ Determination of when a model is deemed unsuccessful
  • Topic 4: Choose appropriate Google Cloud hardware components/ Privacy implications of data usage/ Identifying potential regulatory issues
  • Topic 5: Defining the input (features) and predicted output format/ Modeling techniques given interpretability requirements
  • Topic 6: Batching and streaming data pipelines at scale/ Managing incorrect results/ Identifying nonML solutions
  • Topic 7: Choose appropriate Google Cloud software components/ Assessing and communicating business impact
  • Topic 8: Model performance against baselines, simpler models, and across the time dimension/ Defining outcome of model predictions
  • Topic 9: Training a model as a job in different environments/ Constructing and testing of parameterized pipeline definition in SDK
  • Topic 10: Optimization and simplification of input pipeline for training/ Aligning with Google AI principles and practices
  • Topic 11: Organization and tracking experiments and pipeline runs/ Hooking models into existing CI/CD deployment system

Free Google Google Professional Machine Learning Engineer Exam Actual Questions

The questions for Google Professional Machine Learning Engineer were last updated On Jan. 24, 2023

Question #1

You have built a model that is trained on data stored in Parquet files. You access the data through a Hive table hosted on Google Cloud. You preprocessed these data with PySpark and exported it as a CSV file into Cloud Storage. After preprocessing, you execute additional steps to train and evaluate your model. You want to parametrize this model training in Kubeflow Pipelines. What should you do?

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Correct Answer: D

Question #2

You work on a data science team at a bank and are creating an ML model to predict loan default risk. You have collected and cleaned hundreds of millions of records worth of training data in a BigQuery table, and you now want to develop and compare multiple models on this data using TensorFlow and Vertex AI. You want to minimize any bottlenecks during the data ingestion state while considering scalability. What should you do?

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Correct Answer: B

Question #3

You work for a company that manages a ticketing platform for a large chain of cinemas. Customers use a mobile app to search for movies they're interested in and purchase tickets in the app. Ticket purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline configured to conduct the following steps:

1. Check for availability of the movie tickets at the selected cinema.

2. Assign the ticket price and accept payment.

3. Reserve the tickets at the selected cinema.

4. Send successful purchases to your database.

Each step in this process has low latency requirements (less than 50milliseconds). You have developed a logistic regression model with BigQuery ML that predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase, and this prediction should be added to the ticket purchase process. You want to identify the simplest way to deploy this model to production while adding minimal latency. What should you do?

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Correct Answer: A

Question #4

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?

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Correct Answer: A

Question #5

You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?

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Correct Answer: C

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