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| Section | Objectives |
|---|---|
| Topic 1: Deployment and operations | - Model deployment
|
| Topic 2: ML model development | - Model training and tuning
|
| Topic 3: ML pipeline automation and orchestration | - Pipeline design
|
| Topic 4: Designing ML solutions | - Framing ML problems
|
| Topic 5: Data preparation and processing | - Data ingestion and pipelines
|
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NEW QUESTION # 283
You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. How should you configure the pipeline?
Answer: D
Explanation:
AutoML Natural Language is a service that allows you to quickly build, test and deploy natural language processing (NLP) models without needing to have expertise in NLP or machine learning. You can use it to train a classifier on your corpus of written support cases, and then use the AutoML API to perform classification on new requests. Once the model is trained, it can be deployed as a REST API. This allows the classifier to be integrated into your pipeline and be easily consumed by other systems.
NEW QUESTION # 284
You work at an organization that manages a popular payment app. You built a fraudulent transaction detection model by using scikit-learn and deployed it to a Vertex AI endpoint. The endpoint is currently using
1 e2-standard-2 machine with 2 vCPUs and 8 GB of memory. You discover that traffic on the gateway fluctuates to four times more than the endpoint ' s capacity. You need to address this issue by using the most cost-effective approach. What should you do?
Answer: B
Explanation:
The core requirement here is handling fluctuating traffic (spikes up to 4x capacity) in a cost-effective way.
* Horizontal Autoscaling: Vertex AI endpoints support autoscaling by setting a min-replica-count and a max-replica-count. By increasing the maximum replicas to 6 (Option B), the service can scale out horizontally to meet the 4x traffic demand and, more importantly, scale back down when traffic subsides. This prevents paying for idle resources.
* Why other options are incorrect:
* Option A: TPUs are designed for training large neural networks (TensorFlow/JAX/PyTorch).
They are not supported for standard scikit-learn deployments and would be extremely over- provisioned/expensive for this use case.
* Option C: Switching to a massive 32-vCPU machine (Vertical Scaling) is expensive because you pay for that high capacity 24/7, even when traffic is low. It lacks the elasticity needed for fluctuating loads.
* Option D: Manual intervention based on alerts is slow and prone to downtime during traffic spikes. Vertex AI ' s native autoscaling is the managed, automated way to handle this.
NEW QUESTION # 285
A logistics company needs a forecast model to predict next month's inventory requirements for a single item in
10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.
Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)
Answer: A,C
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/forecast/latest/dg/forecast.dg.pdf
NEW QUESTION # 286
Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:
You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets. How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?
Answer: B
Explanation:
If we just put inside the Training set, Validation set and Test set, randomly Text, Paragraph or sentences the model will have the ability to learn specific qualities about The Author's use of language beyond just his own articles. Therefore the model will mixed up different opinions.
Rather if we divided things up a the author level, so that given authors were only on the training data, or only in the test data or only in the validation data. The model will find more difficult to get a high accuracy on the test validation (What is correct and have more sense!). Because it will need to really focus in author by author articles rather than get a single political affiliation based on a bunch of mixed articles from different authors.
https://developers.google.com/machine-learning/crash-course/18th-century-literature
NEW QUESTION # 287
You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?
Answer: A
Explanation:
Vertex ML Metadata is a service that lets you track and manage the metadata produced by your ML workflows in a centralized way. It helps you have reproducible experiments by generating artifacts that represent the data, parameters, and metrics used or produced by your ML system. You can also analyze the lineage and performance of your ML artifacts using Vertex ML Metadata.
Some of the benefits of using Vertex ML Metadata are:
* It captures your ML system's metadata as a graph, where artifacts and executions are nodes, and events are edges that link them as inputs or outputs.
* It allows you to create contexts to group sets of artifacts and executions together, such as experiments, runs, or projects.
* It supports querying and filtering the metadata using the Vertex AI SDK for Python or REST commands.
* It integrates with other Vertex AI services, such as Vertex AI Pipelines and Vertex AI Experiments, to automatically log metadata and artifacts.
The other options are not suitable for tracking and managing ML metadata in a centralized way.
* Option A: Storing your tf.logging data in BigQuery is not enough to capture the full metadata of your ML system, such as the artifacts and their lineage. BigQuery is a data warehouse service that is mainly used for analytics and reporting, not for metadata management.
* Option B: Managing all relational entities in the Hive Metastore is not a good solution for ML metadata, as it is designed for storing metadata of Hive tables and partitions, not for ML artifacts and executions.
Hive Metastore is a component of the Apache Hive project, which is a data warehouse system for querying and analyzing large datasets stored in Hadoop.
* Option C: Storing all ML metadata in Google Cloud's operations suite is not a feasible option, as it is a set of tools for monitoring, logging, tracing, and debugging your applications and infrastructure, not for ML metadata. Google Cloud's operations suite does not provide the features and integrations that Vertex ML Metadata offers for ML workflows.
NEW QUESTION # 288
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