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Google Professional Machine Learning Engineer Sample Questions (Q79-Q84):

NEW QUESTION # 79
You work on the data science team for a multinational beverage company. You need to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations. You are provided with historical data that includes product types, product sales volumes, expenses, and profits for all regions. What should you use as the input and output for your model?

• A. Use latitude, longitude, and product type as features. Use revenue and expenses as model outputs.
• B. Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use revenue and expenses as model outputs.
• C. Use latitude, longitude, and product type as features. Use profit as model output.
• D. Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use profit as model output.

Answer: D
NEW QUESTION # 80
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?

• A. Search for duplicate accounts in the AWS Glue Data Catalog.
• B. Execute the built-in FindDuplicates Amazon Athena query.
• C. Create an AWS Glue crawler to infer duplicate accounts in the source data.
• D. Create a FindMatches machine learning transform in AWS Glue.

Answer: D Explanation:
Explanation/Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html
NEW QUESTION # 81
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?

• A. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)
• B. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
• C. Load the data into BigQuery and read the data from BigQuery.
• D. Load the data into Cloud Bigtable, and read the data from Bigtable

Answer: B
NEW QUESTION # 82
You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?

• A. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
• B. Significantly increase the maxenqueuedbatches TensorFlow Serving parameter
• C. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes
• D. Significantly increase the maxbatchsize TensorFlow Serving parameter

Answer: D
NEW QUESTION # 83
You work as an ML engineer at a social media company, and you are developing a visual filter for users' profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company's iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones. What should you do?

• A. Train a model using AutoML Vision and use the "export for TensorFlow.js" option.
• B. Train a model using AutoML Vision and use the "export for Coral" option.
• C. Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).
• D. Train a model using AutoML Vision and use the "export for Core ML" option.

Answer: D
NEW QUESTION # 84
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