Google Cloud

Google Cloud Certified – Professional Machine Learning Engineer

PR000269
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Validate your skills as a Professional Machine Learning Engineer with exam code PR000269 in Google Cloud.

495 questions 0 views Free
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Questions 51–60 of 495

Q51

What happens when using a high capacity GPU with no optimizations in model training?

  • A Increased training speed
  • B Improved model accuracy
  • C Higher cost without benefits
  • D Fewer data preprocessing steps
Explanation Using high capacity without optimizations can lead to unnecessary costs without corresponding improvements in performance or accuracy.
Q52

Which service is best for deploying machine learning models in real-time?

  • A Google Cloud AI Platform
  • B Google Cloud Storage
  • C Google Compute Engine
  • D Google App Engine
Explanation AI Platform is designed for model deployment, while others are for general compute or storage needs.
Q53

A company needs to analyze large datasets without overspending on infrastructure. Which option is most effective?

  • A BigQuery with preemptible VM usage
  • B Cloud Spanner
  • C Compute Engine with custom VMs
  • D Cloud Memorystore
Explanation BigQuery is optimized for large-scale analytics; preemptible VMs help reduce costs.
Q54

What happens when you train a neural network without any regularization?

  • A Underfitting occurs
  • B Overfitting can happen
  • C Convergence becomes faster
  • D Bias increases significantly
Explanation Without regularization, models are likely to memorize the training data leading to overfitting.
Q55

Which Google Cloud service is best for deploying machine learning models at scale?

  • A AI Platform
  • B Cloud SQL
  • C BigQuery
  • D Cloud Pub/Sub
Explanation AI Platform is designed specifically for deploying machine learning models, while the other options serve different purposes.
Q56

A company needs to analyze real-time streaming data from IoT devices. What service should they use?

  • A Cloud Functions
  • B Dataflow
  • C Cloud Storage
  • D Cloud Datastore
Explanation Dataflow can handle real-time data streams effectively, while the others are not optimized for real-time processing.
Q57

What happens when you convert a float64 value to int64 in Google Cloud Dataflow?

  • A Value rounds up
  • B Value rounds down
  • C Value truncates decimals
  • D Value results in an error
Explanation Converting float64 to int64 truncates the decimal part without rounding, while the other options represent incorrect behaviors.
Q58

Which service simplifies deploying machine learning models in Google Cloud?

  • A Vertex AI
  • B Cloud Functions
  • C Dataflow
  • D BigQuery
Explanation Vertex AI is specifically designed for deploying ML models; the others serve different purposes.
Q59

A company needs to manage access to its ML resources securely. What is the best practice?

  • A Use IAM roles to control access.
  • B Open all access to datasets.
  • C Share service account keys.
  • D Use public datasets only.
Explanation Using IAM roles allows for fine-grained control over resource access.
Q60

What happens when you set a model's serving traffic percentage to zero in Vertex AI?

  • A Model is deleted immediately.
  • B No predictions are served.
  • C Model is still active.
  • D Traffic reroutes to another model.
Explanation Setting the traffic percentage to zero disables serving but doesn't delete the model.