Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam braindumps download (PROFESSIONAL MACHINE LEARNING ENGINEER exam dumps Free)

QUESTION: 91

You have been given a dataset with sales predictions based on your company’s marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the data. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks. You only have a few hours to gather the results of your experiments.
Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?

Use BigQuery ML to run several regression models, and analyze their performance.
Read the data from BigQuery using Dataproc, and run several models using SparkML.
Use Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics.
Train a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.

Answer(s): A

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QUESTION: 92

You have recently used TensorFlow to train a classification model on tabular data. You have created a Dataflow pipeline that can transform several terabytes of data into training or prediction datasets consisting of TFRecords. You now need to productionize the model, and you want the predictions to be automatically uploaded to a BigQuery table on a weekly schedule.
What should you do?

Import the model into Vertex AI and deploy it to a Vertex AI endpoint. On Vertex AI Pipelines, create a pipeline that uses the DataflowPythonJobOp and the ModelBatchPredictOp components.
Import the model into Vertex AI and deploy it to a Vertex AI endpoint. Create a Dataflow pipeline that reuses the data processing logic sends requests to the endpoint, and then uploads predictions to a BigQuery table.
Import the model into Vertex AI. On Vertex AI Pipelines, create a pipeline that uses the DataflowPythonJobOp and the ModelBatchPredictOp components.
Import the model into BigQuery. Implement the data processing logic in a SQL query. On Vertex AI Pipelines create a pipeline that uses the BigqueryQueryJobOp and the BigqueryPredictModelJobOp components.

Answer(s): C

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QUESTION: 93

You are building an ML model to predict trends in the stock market based on a wide range of factors.
While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don’t overfit the model.
What should you do?

Standardize the data by transforming it with a logarithmic function.
Apply a principal component analysis (PCA) to minimize the effect of any particular feature.
Use a binning strategy to replace the magnitude of each feature with the appropriate bin number.
Normalize the data by scaling it to have values between 0 and 1.

Answer(s): D

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QUESTION: 94

You are using transfer learning to train an image classifier based on a pre-trained EfficientNet model. Your training dataset has 20,000 images. You plan to retrain the model once per day. You need to minimize the cost of infrastructure.
What platform components and configuration environment should you use?

A Deep Learning VM with 4 V100 GPUs and local storage.
A Deep Learning VM with 4 V100 GPUs and Cloud Storage.
A Google Kubernetes Engine cluster with a V100 GPU Node Pool and an NFS Server
An AI Platform Training job using a custom scale tier with 4 V100 GPUs and Cloud Storage

Answer(s): D

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QUESTION: 95

You have trained a text classification model in TensorFlow using AI Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead.
What should you do?

Export the model to BigQuery ML.
Deploy and version the model on AI Platform.
Use Dataflow with the SavedModel to read the data from BigQuery.
Submit a batch prediction job on AI Platform that points to the model location in Cloud Storage.

Answer(s): A

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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps Professional Machine Learning Engineer Questions 91–95 (page: 20)

QUESTION: 91

QUESTION: 92

QUESTION: 93

QUESTION: 94

QUESTION: 95

Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 91–95 (page: 20)