QUESTION: 81

Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

Vertex AI Pipelines and App Engine
Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring
Cloud Composer, BigQuery ML, and Vertex AI Prediction
Cloud Composer, Vertex AI Training with custom containers, and App Engine

Answer(s): A

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QUESTION: 82

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?

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

Answer(s): D

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QUESTION: 83

You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor’s data from the past 12 hours. How should you design the architecture?

1. HTTP requests are sent by the sensors to your ML model, which is deployed as a microservice and exposes a REST API for prediction
2. Your application queries a Vertex AI endpoint where you deployed your model.
3. Responses are received by the caller application as soon as the model produces the prediction.
1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline.
2. The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic.
3. Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.
1. Export your data to Cloud Storage using Dataflow.
2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.
3. Export the batch prediction job outputs from Cloud Storage and import them into Cloud SQL.
1. Export the data to Cloud Storage using the BigQuery command-line tool
2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.
3. Export the batch prediction job outputs from Cloud Storage and import them into BigQuery.

Answer(s): C

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QUESTION: 84

Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendation model that will suggest articles to readers that are similar to the articles they are currently reading. Which approach should you use?

Create a collaborative filtering system that recommends articles to a user based on the user’s past behavior.
Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity.
Build a logistic regression model for each user that predicts whether an article should be recommended to a user.
Manually label a few hundred articles, and then train an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories.

Answer(s): A

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Free Professional Machine Learning Engineer Exam Braindumps (page: 21)

QUESTION: 81

QUESTION: 82

QUESTION: 83

QUESTION: 84