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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 31–35 (page: 8)

Updated On: 25-Oct-2026
Page 8 of 82
PDF with 402 Questions

You work for a manufacturing company. You need to train a custom image classification model to detect product defects at the end of an assembly line. Although your model is performing well, some images in your holdout set are consistently mislabeled with high confidence. You want to use Vertex AI to understand your model’s results.
What should you do?

  1. Configure feature-based explanations by using Integrated Gradients. Set visualization type to PIXELS, and set clip_percent_upperbound to 95.
  2. Create an index by using Vertex AI Matching Engine. Query the index with your mislabeled images.
  3. Configure feature-based explanations by using XRAI. Set visualization type to OUTLINES, and set polarity to positive.
  4. Configure example-based explanations. Specify the embedding output layer to be used for the latent space representation.

Answer(s): D



You are an ML engineer at a manufacturing company. You are creating a classification model for a predictive maintenance use case. You need to predict whether a crucial machine will fail in the next three days so that the repair crew has enough time to fix the machine before it breaks. Regular maintenance of the machine is relatively inexpensive, but a failure would be very costly. You have trained several binary classifiers to predict whether the machine will fail, where a prediction of 1 means that the ML model predicts a failure.
You are now evaluating each model on an evaluation dataset. You want to choose a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure.
Which model should you choose?

  1. The model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0.5
  2. The model with the lowest root mean squared error (RMSE) and recall greater than 0.5.
  3. The model with the highest recall where precision is greater than 0.5.
  4. The model with the highest precision where recall is greater than 0.5.

Answer(s): C



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?

  1. Remove the data transformation step from your pipeline.
  2. Containerize the PySpark transformation step, and add it to your pipeline.
  3. Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.
  4. Deploy Apache Spark at a separate node pool in a Google Kubernetes Engine cluster. Add a ContainerOp to your pipeline that invokes a corresponding transformation job for this Spark instance.

Answer(s): C



You are profiling the performance of your TensorFlow model training time and notice a performance issue caused by inefficiencies in the input data pipeline for a single 5 terabyte CSV file dataset on Cloud Storage. You need to optimize the input pipeline performance.
Which action should you try first to increase the efficiency of your pipeline?

  1. Preprocess the input CSV file into a TFRecord file.
  2. Randomly select a 10 gigabyte subset of the data to train your model.
  3. Split into multiple CSV files and use a parallel interleave transformation.
  4. Set the reshuffle_each_iteration parameter to true in the tf.data.Dataset.shuffle method.

Answer(s): C



You are developing a Gemini-based onboarding chatbot to answer users’ questions based on five PDF onboarding documents in Cloud Storage. The documents are updated weekly or monthly as needed. You expect that the application will receive high-volume, repeated user queries against this documentation context.
You need to use the most appropriate and cost-effective solution for managing the context and ensuring that the chatbot has up-to-date information.
What should you do?

  1. Fine-tune a Gemini model using the text from the five PDF files as a training dataset.
  2. Send requests with the five PDF files as content pointing to the URI file. Use implicit context caching.
  3. Create an explicit context cache using the five PDF files, and regenerate the cache when the source files change.
  4. Create an explicit context cache using the five PDF files. Set a time-to-live (TTL) of seven days, and regenerate the cache every seven days.

Answer(s): C

Explanation:

Explicit context caching is designed for high-volume requests that repeatedly use the same large document context, reducing input-processing costs and latency. Recreating the cache whenever the source PDFs change ensures that responses remain grounded in the latest onboarding information without paying to process all five documents with every request.


Reference:

https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/context-cache/context-cache-overview https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/context-cache/context-cache-create https://docs.cloud.google.com/vertex-ai/generative-ai/docs/samples/googlegenaisdk-contentcache-create-with-txt-gcs-pdf



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