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Google Professional-Machine-Learning-Engineer Practice Test Questions Answers

Exam Code: Professional-Machine-Learning-Engineer (Updated 285 Q&As with Explanation)
Exam Name: Google Professional Machine Learning Engineer
Last Update: 30-Aug-2025
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Questions Include:

  • Single Choice: 281 Q&A's
  • Multiple Choice: 4 Q&A's

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    Professional-Machine-Learning-Engineer Questions and Answers

    Question # 1

    You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?

    Choose 2 answers

    A.

    Use the interleave option for reading data

    B.

    Reduce the value of the repeat parameter

    C.

    Increase the buffer size for the shuffle option.

    D.

    Set the prefetch option equal to the training batch size

    E.

    Decrease the batch size argument in your transformation

    Question # 2

    You are developing a custom TensorFlow classification model based on tabular data. Your raw data is stored in BigQuery contains hundreds of millions of rows, and includes both categorical and numerical features. You need to use a MaxMin scaler on some numerical features, and apply a one-hot encoding to some categorical features such as SKU names. Your model will be trained over multiple epochs. You want to minimize the effort and cost of your solution. What should you do?

    A.

    1 Write a SQL query to create a separate lookup table to scale the numerical features.

    2. Deploy a TensorFlow-based model from Hugging Face to BigQuery to encode the text features.

    3. Feed the resulting BigQuery view into Vertex Al Training.

    B.

    1 Use BigQuery to scale the numerical features.

    2. Feed the features into Vertex Al Training.

    3 Allow TensorFlow to perform the one-hot text encoding.

    C.

    1 Use TFX components with Dataflow to encode the text features and scale the numerical features.

    2 Export results to Cloud Storage as TFRecords.

    3 Feed the data into Vertex Al Training.

    D.

    1 Write a SQL query to create a separate lookup table to scale the numerical features.

    2 Perform the one-hot text encoding in BigQuery.

    3. Feed the resulting BigQuery view into Vertex Al Training.

    Question # 3

    You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?

    Choose 2 answers

    A.

    Decrease the number of parallel trials

    B.

    Decrease the range of floating-point values

    C.

    Set the early stopping parameter to TRUE

    D.

    Change the search algorithm from Bayesian search to random search.

    E.

    Decrease the maximum number of trials during subsequent training phases.

    Question # 4

    You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

    A.

    Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately

    Choose an automatic data split across the training, validation, and testing sets

    B.

    Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate

    transformations Choose an automatic data split across the training, validation, and testing sets

    C.

    Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets

    D.

    Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set

    Question # 5

    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 Al Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the Al 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

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