Free Access to Google.Professional-Machine-Learning-Engineer.v2024-01-19.q113 with Valid Practice Test (Page 18)

Question 81

You work for an online grocery store. You recently developed a custom ML model that recommends a recipe when a user arrives at the website. You chose the machine type on the Vertex Al endpoint to optimize costs by using the queries per second (QPS) that the model can serve, and you deployed it on a single machine with 8 vCPUs and no accelerators.
A holiday season is approaching and you anticipate four times more traffic during this time than the typical daily traffic You need to ensure that the model can scale efficiently to the increased demand. What should you do?

A.1, Maintain the same machine type on the endpoint.
2 Set up a monitoring job and an alert for CPU usage
3 If you receive an alert add a compute node to the endpoint
B.1 Change the machine type on the endpoint to have 32 vCPUs
2. Set up a monitoring job and an alert for CPU usage
3 If you receive an alert, scale the vCPUs further as needed
C.1 Maintain the same machine type on the endpoint Configure the endpoint to enable autoscalling based on vCPU usage.
2 Set up a monitoring job and an alert for CPU usage
3 If you receive an alert investigate the cause
D.1 Change the machine type on the endpoint to have a GPU_ Configure the endpoint to enable autoscaling based on the GPU usage.
2 Set up a monitoring job and an alert for GPU usage.
3 If you receive an alert investigate the cause.

Correct Answer: C

Vertex AI Endpoint is a service that allows you to serve your ML models online and scale them automatically.
You can use Vertex AI Endpoint to deploy the custom ML model that you developed for recommending recipes to the users. You can maintain the same machine type on the endpoint, which is a single machine with
8 vCPUs and no accelerators. This machine type can optimize the costs by using the queries per second (QPS) that the model can serve. You can also configure the endpoint to enable autoscaling based on vCPU usage.
Autoscaling is a feature that allows the endpoint to adjust the number of compute nodes based on the traffic demand. By enabling autoscaling based on vCPU usage, you can ensure that the endpoint can scale efficiently to the increased demand during the holiday season, without overprovisioning or underprovisioning the resources. You can also set up a monitoring job and an alert for CPU usage. Monitoring is a service that allows you to collect and analyze the metrics and logs from your Google Cloud resources. You can use Monitoring to monitor the CPU usage of your endpoint, which is an indicator of the load and performance of your model.
You can also set up an alert for CPU usage, which is a feature that allows you to receive notifications when the CPU usage exceeds a certain threshold. By setting up a monitoring job and an alert for CPU usage, you can keep track of the health and status of your endpoint, and detect any issues or anomalies. If you receive an alert, you can investigate the cause by using the Monitoring dashboard, which provides a graphical interface for viewing and analyzing the metrics and logs from your endpoint. You can also use the Monitoring dashboard to troubleshoot and resolve the issues, such as adjusting the autoscaling parameters, optimizing the model, or updating the machine type. By using Vertex AI Endpoint, autoscaling, and Monitoring, you can ensure that the model can scale efficiently to the increased demand during the holiday season, and handle any issues or alerts that might arise. References:
* [Vertex AI Endpoint documentation]
* [Autoscaling documentation]
* [Monitoring documentation]
* [Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]

Question 82

You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?

A.Compare the loss performance for each model on a held-out dataset.
B.Compare the loss performance for each model on the validation data
C.Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool
D.Compare the mean average precision across the models using the Continuous Evaluation feature

Question 83

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?

A.Cloud Composer, BigQuery ML , and Al Platform Prediction
B.Cloud Composer, Al Platform Training with custom containers , and App Engine
C.Kubeflow Pipelines and Al Platform Prediction
D.Kubeflow Pipelines and App Engine

Question 84

A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.
The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.
Which solution should the Data Scientist build to satisfy the requirements?

A.Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
B.Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
C.Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL database. Have the Analysts query and run dashboards from the RDS database.
D.Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.

Question 85

You are an ML engineer at an ecommerce company and have been tasked with building a model that predicts how much inventory the logistics team should order each month. Which approach should you take?

A.Use a clustering algorithm to group popular items together. Give the list to the logistics team so they can increase inventory of the popular items.
B.Use a regression model to predict how much additional inventory should be purchased each month. Give the results to the logistics team at the beginning of the month so they can increase inventory by the amount predicted by the model.
C.Use a time series forecasting model to predict each item's monthly sales. Give the results to the logistics team so they can base inventory on the amount predicted by the model.
D.Use a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKED. Give the report to the logistics team each month so they can fine-tune inventory levels.

Correct Answer: C

The best approach to build a model that predicts how much inventory the logistics team should order each month is to use a time series forecasting model to predict each item's monthly sales. This approach can capture the temporal patterns and trends in the sales data, such as seasonality, cyclicality, and autocorrelation. It can also account for the variability and uncertainty in the demand, and provide confidence intervals and error metrics for the predictions. By using a time series forecasting model, you can provide the logistics team with accurate and reliable estimates of the future sales for each item, which can help them optimize the inventory levels and avoid overstocking or understocking. You can use various methods and tools to build a time series forecasting model, such as ARIMA, LSTM, Prophet, or BigQuery ML.
The other options are not optimal for the following reasons:
* A. Using a clustering algorithm to group popular items together is not a good approach, as it does not provide any quantitative or temporal information about the sales or the inventory. It only provides a qualitative and static categorization of the items based on their similarity or dissimilarity. Moreover, clustering is an unsupervised learning technique, which does not use any target variable or feedback to guide the learning process. This can result in arbitrary and inconsistent clusters, which may not reflect the true demand or preferences of the customers.
* B. Using a regression model to predict how much additional inventory should be purchased each month is not a good approach, as it does not account for the individual differences and dynamics of each item.
It only provides a single aggregated value for the whole inventory, which can be misleading and inaccurate. Moreover, a regression model is not well-suited for handling time series data, as it assumes that the data points are independent and identically distributed, which is not the case for sales data. A regression model can also suffer from overfitting or underfitting, depending on the choice and complexity of the features and the model.
* D. Using a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKED is not a good approach, as it does not provide any numerical or predictive information about the sales or the inventory. It only provides a discrete and subjective label for the inventory levels, which can be vague and ambiguous. Moreover, a classification model is not well-suited for handling time series data, as it assumes that the data points are independent and identically distributed, which is not the case for sales data. A classification model can also suffer
* from class imbalance, misclassification, or overfitting, depending on the choice and complexity of the features, the model, and the threshold.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* Time Series Forecasting: Principles and Practice
* BigQuery ML: Time series analysis

Question 81

Question 82

Question 83

Question 84

Question 85

Download PDF File