TECHNICAL GOOGLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER TRAINING | LATEST PROFESSIONAL-MACHINE-LEARNING-ENGINEER TEST VCE

Technical Google Professional-Machine-Learning-Engineer Training | Latest Professional-Machine-Learning-Engineer Test Vce

Technical Google Professional-Machine-Learning-Engineer Training | Latest Professional-Machine-Learning-Engineer Test Vce

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Google Professional Machine Learning Engineer Sample Questions (Q93-Q98):

NEW QUESTION # 93
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

  • A. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
  • B. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
  • C. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
  • D. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.

Answer: C

Explanation:
Developing ML models with AI Platform for image segmentation on CT scans requires a lot of computation and experimentation, as image segmentation is a complex and challenging task that involves assigning a label to each pixel in an image. Image segmentation can be used for various medical applications, such as tumor detection, organ segmentation, or lesion localization1 To minimize the computation costs and manual intervention while having version control for the code, one should use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository. Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Cloud Source Repositories, Cloud Storage, GitHub, or Bitbucket, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives2 Cloud Build allows you to set up automated triggers that start a build when changes are pushed to a source code repository. You can configure triggers to filter the changes based on the branch, tag, or file path3 Cloud Source Repositories is a service that provides fully managed private Git repositories on Google Cloud Platform. Cloud Source Repositories allows you to store, manage, and track your code using the Git version control system. You can also use Cloud Source Repositories to connect to other Google Cloud services, such as Cloud Build, Cloud Functions, or Cloud Run4 To use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository, you need to do the following steps:
* Create a Cloud Source Repository for your code, and push your code to the repository. You can use the Cloud SDK, Cloud Console, or Cloud Source Repositories API to create and manage your repository5
* Create a Cloud Build trigger for your repository, and specify the build configuration and the trigger settings. You can use the Cloud SDK, Cloud Console, or Cloud Build API to create and manage your trigger.
* Specify the steps of the build in a YAML or JSON file, such as installing the dependencies, running the tests, building the container image, and submitting the training job to AI Platform. You can also use the Cloud Build predefined or custom build steps to simplify your build configuration.
* Push your new code to the repository, and the trigger will start the build automatically. You can monitor the status and logs of the build using the Cloud SDK, Cloud Console, or Cloud Build API.
The other options are not as easy or feasible. Using Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job is not ideal, as Cloud Functions has limitations on the memory, CPU, and execution time, and does not provide a user interface for managing and tracking your builds. Using the gcloud command-line tool to submit training jobs on AI Platform when you update your code is not optimal, as it requires manual intervention and does not leverage the benefits of Cloud Build and its integration with Cloud Source Repositories. Creating an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor is not relevant, as Cloud Composer is mainly designed for orchestrating complex workflows across multiple systems, and does not provide a version control system for your code.
References: 1: Image segmentation 2: Cloud Build overview 3: Creating and managing build triggers 4: Cloud Source Repositories overview 5: Quickstart: Create a repository : [Quickstart: Create a build trigger] :
[Configuring builds] : [Viewing build results]


NEW QUESTION # 94
You work for an online travel agency that also sells advertising placements on its website to other companies.
You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor. You want to Implement the simplest solution. How should you configure the prediction pipeline?

  • A. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user's navigation context, and then deploy the model on AI Platform Prediction.
  • B. Embed the client on the website, and then deploy the model on AI Platform Prediction.
  • C. Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.
  • D. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user's navigation context, and then deploy the model on Google Kubernetes Engine.

Answer: B

Explanation:
In this scenario, the goal is to predict the most relevant web banner that a user should see next on an online travel agency's website. The model needs to have low latency requirements of 300ms@p99, and there are thousands of web banners to choose from. The exploratory analysis has shown that the navigation context is a good predictor. Security is also important to the company. Given these requirements, the best configuration for the prediction pipeline would be to embed the client on the website and deploy the model on AI Platform Prediction. Option A is the correct answer.
Option A: Embed the client on the website, and then deploy the model on AI Platform Prediction. This option is the simplest solution that meets the requirements. The client can collect the user's navigation context and send it to the model deployed on AI Platform Prediction for prediction. AI Platform Prediction can handle large-scale prediction requests and has low latency requirements. This option does not require any additional infrastructure or services, making it the simplest solution.
Option B: Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction. This option adds an additional layer of infrastructure by deploying the gateway on App Engine. While App Engine can handle large-scale requests, it adds complexity to the pipeline and may not be necessary for this use case.
Option C: Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user's navigation context, and then deploy the model on AI Platform Prediction. This option adds even more complexity to the pipeline by deploying the database on Cloud Bigtable. While Cloud Bigtable can provide fast and scalable access to the user's navigation context, it may not be needed for this use case. Moreover, Cloud Bigtable may introduce additional latency and cost to the pipeline.
Option D: Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user's navigation context, and then deploy the model on Google Kubernetes Engine. This option is the most complex and costly solution that does not meet the requirements. Deploying the model on Google Kubernetes Engine requires more management and configuration than AI Platform Prediction. Moreover, Google Kubernetes Engine may not be able to meet the low latency requirements of 300ms@p99. Deploying the database on Memorystore also adds unnecessary overhead and cost to the pipeline.
Reference:
AI Platform Prediction documentation
App Engine documentation
Cloud Bigtable documentation
[Memorystore documentation]
[Google Kubernetes Engine documentation]


NEW QUESTION # 95
A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream.
As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.
Which next step is MOST likely to improve the data ingestion rate into Amazon S3?

  • A. Decrease the retention period for the data stream.
  • B. Add more consumers using the Kinesis Client Library (KCL).
  • C. Increase the number of S3 prefixes for the delivery stream to write to.
  • D. Increase the number of shards for the data stream.

Answer: D

Explanation:
Explanation/Reference:


NEW QUESTION # 96
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?

  • A. Send incoming prediction requests to a Pub/Sub topic
    * Transform the incoming data using a Dataflow job
    * Submit a prediction request to Al Platform using the transformed data
    * Write the predictions to an outbound Pub/Sub queue
  • B. Validate the accuracy of the model that you trained on preprocessed data
    * Create a new model that uses the raw data and is available in real time
    * Deploy the new model onto Al Platform for online prediction
  • C. Stream incoming prediction request data into Cloud Spanner
    * Create a view to abstract your preprocessing logic.
    * Query the view every second for new records
    * Submit a prediction request to Al Platform using the transformed data
    * Write the predictions to an outbound Pub/Sub queue.
  • D. Send incoming prediction requests to a Pub/Sub topic
    * Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
    * Implement your preprocessing logic in the Cloud Function
    * Submit a prediction request to Al Platform using the transformed data
    * Write the predictions to an outbound Pub/Sub queue

Answer: D


NEW QUESTION # 97
An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

  • A. Last observation carried forward
  • B. Listwise deletion
  • C. Multiple imputation
  • D. Mean substitution

Answer: C

Explanation:
Explanation/Reference: https://worldwidescience.org/topicpages/i/imputing+missing+values.html


NEW QUESTION # 98
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