BRAINDUMPS GOOGLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER PDF, KNOWLEDGE PROFESSIONAL-MACHINE-LEARNING-ENGINEER POINTS

Braindumps Google Professional-Machine-Learning-Engineer Pdf, Knowledge Professional-Machine-Learning-Engineer Points

Braindumps Google Professional-Machine-Learning-Engineer Pdf, Knowledge Professional-Machine-Learning-Engineer Points

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Obtaining the Google Professional Machine Learning Engineer certification can be a significant advantage for individuals seeking employment opportunities in the field of machine learning. Google Professional Machine Learning Engineer certification demonstrates to employers that an individual has a strong understanding of machine learning concepts and is capable of designing and implementing machine learning models on the Google Cloud Platform. Additionally, this certification can lead to higher salaries, increased job opportunities, and advancement within an organization.

Google Professional Machine Learning Engineer certification is a challenging yet rewarding exam that provides candidates with the opportunity to showcase their expertise in machine learning. Google Professional Machine Learning Engineer certification is ideal for individuals who are seeking to advance their careers in this field and want to gain recognition for their skills and knowledge. With this certification, candidates can demonstrate their proficiency in machine learning and position themselves as experts in this rapidly growing field.

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Google Professional Machine Learning Engineer certification exam is a great way for professionals to showcase their expertise in designing and developing machine learning models on Google Cloud Platform. Google Professional Machine Learning Engineer certification exam covers various topics related to machine learning, and passing the exam demonstrates the individual's ability to use Google Cloud Platform tools and services to create scalable and efficient machine learning models. Google Professional Machine Learning Engineer certification exam is a credible and recognized way for professionals to demonstrate their skills and knowledge in the field of machine learning.

Google Professional Machine Learning Engineer Sample Questions (Q78-Q83):

NEW QUESTION # 78
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?

  • A. Store all ML metadata in Google Cloud's operations suite.
  • B. Store your tf.logging data in BigQuery.
  • C. Manage all relational entities in the Hive Metastore.
  • D. Manage your ML workflows with Vertex ML Metadata.

Answer: D

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 # 79
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano. Scikit-team, and custom libraries. What should you do?

  • A. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
  • B. Create a library of VM images on Compute Engine; and publish these images on a centralized repository
  • C. Use the Al Platform custom containers feature to receive training jobs using any framework
  • D. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob

Answer: C

Explanation:
A cloud-based backend system is a system that runs on a cloud platform and provides services or resources to other applications or users. A cloud-based backend system can be used to submit training jobs, which are tasks that involve training a machine learning model on a given dataset using a specific framework and configuration1 However, a cloud-based backend system can also have some drawbacks, such as:
* High maintenance: A cloud-based backend system may require a lot of administration and management, such as provisioning, scaling, monitoring, and troubleshooting the cloud resources and services. This can be time-consuming and costly, and may distract from the core business objectives2
* Low flexibility: A cloud-based backend system may not support all the frameworks and libraries that the data scientists need to use for their training jobs. This can limit the choices and capabilities of the data scientists, and affect the quality and performance of their models3
* Poor integration: A cloud-based backend system may not integrate well with other cloud services or
* tools that the data scientists need to use for their machine learning workflows, such as data processing, model deployment, or model monitoring. This can create compatibility and interoperability issues, and reduce the efficiency and productivity of the data scientists.
Therefore, it may be better to use a managed service instead of a cloud-based backend system to submit training jobs. A managed service is a service that is provided and operated by a third-party provider, and offers various benefits, such as:
* Low maintenance: A managed service handles the administration and management of the cloud resources and services, and abstracts away the complexity and details of the underlying infrastructure. This can save time and money, and allow the data scientists to focus on their core tasks2
* High flexibility: A managed service can support multiple frameworks and libraries that the data scientists need to use for their training jobs, and allow them to customize and configure their training environments and parameters. This can enhance the choices and capabilities of the data scientists, and improve the quality and performance of their models3
* Easy integration: A managed service can integrate seamlessly with other cloud services or tools that the data scientists need to use for their machine learning workflows, and provide a unified and consistent interface and experience. This can solve the compatibility and interoperability issues, and increase the efficiency and productivity of the data scientists.
One of the best options for using a managed service to submit training jobs is to use the AI Platform custom containers feature to receive training jobs using any framework. AI Platform is a Google Cloud service that provides a platform for building, deploying, and managing machine learning models. AI Platform supports various machine learning frameworks, such as TensorFlow, PyTorch, scikit-learn, and XGBoost, and provides various features, such as hyperparameter tuning, distributed training, online prediction, and model monitoring.
The AI Platform custom containers feature allows the data scientists to use any framework or library that they want for their training jobs, and package their training application and dependencies as a Docker container image. The data scientists can then submit their training jobs to AI Platform, and specify the container image and the training parameters. AI Platform will run the training jobs on the cloud infrastructure, and handle the scaling, logging, and monitoring of the training jobs. The data scientists can also use the AI Platform features to optimize, deploy, and manage their models.
The other options are not as suitable or feasible. Configuring Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob is not ideal, as Kubeflow is mainly designed for TensorFlow-based training jobs, and does not support other frameworks or libraries. Creating a library of VM images on Compute Engine and publishing these images on a centralized repository is not optimal, as Compute Engine is a low-level service that requires a lot of administration and management, and does not provide the features and integrations of AI Platform. Setting up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure is not relevant, as Slurm is a tool for managing and scheduling jobs on a cluster of nodes, and does not provide a managed service for training jobs.
References: 1: Cloud computing 2: Managed services 3: Machine learning frameworks : [Machine learning workflow] : [AI Platform overview] : [Custom containers for training]


NEW QUESTION # 80
You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?

  • A. This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.
  • B. This is a good result because the accuracy across both groups is greater than 80%.
  • C. This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.
  • D. This is not a good result because the model is performing worse than predicting that people will always renew their subscription.

Answer: D

Explanation:
This is not a good result because the model is performing worse than predicting that people will always renew their subscription. This option has the following reasons:
It indicates that the model is not learning from the data, but rather memorizing the majority class. Since 90% of the individuals renew their subscription every year, the model can achieve a 90% accuracy by simply predicting that everyone will renew their subscription, without considering the features or the patterns in the data. However, the model's accuracy for predicting those who renew their subscription is only 82%, which is lower than the baseline accuracy of 90%. This suggests that the model is overfitting to the minority class (those who cancel their subscription), and underfitting to the majority class (those who renew their subscription).
It implies that the model is not useful for the business problem, as it cannot identify the customers who are at risk of churning. The goal of predicting whether customers will cancel their annual subscription is to prevent customer churn and increase customer retention. However, the model's accuracy for predicting those who cancel their subscription is 99%, which is too high and unrealistic, as it means that the model can almost perfectly identify the customers who will churn, without any false positives or false negatives. This may indicate that the model is cheating or exploiting some leakage in the data, such as a feature that reveals the outcome of the prediction. Moreover, the model's accuracy for predicting those who renew their subscription is 82%, which is too low and unreliable, as it means that the model can miss many customers who will churn, and falsely label them as renewing customers. This can lead to losing customers and revenue, and failing to take proactive actions to retain them.
Reference:
How to Evaluate Machine Learning Models: Classification Metrics | Machine Learning Mastery Imbalanced Classification: Predicting Subscription Churn | Machine Learning Mastery


NEW QUESTION # 81
You are going to train a DNN regression model with Keras APIs using this code:

How many trainable weights does your model have? (The arithmetic below is correct.)

  • A. 501*256+257*128+128*2=161408
  • B. 501*256+257*128+2 = 161154
  • C. 500*256+256*128+128*2 = 161024
  • D. 500*256*0 25+256*128*0 25+128*2 = 40448

Answer: D


NEW QUESTION # 82
You work for a company that is developing an application to help users with meal planning You want to use machine learning to scan a corpus of recipes and extract each ingredient (e g carrot, rice pasta) and each kitchen cookware (e.g. bowl, pot spoon) mentioned Each recipe is saved in an unstructured text file What should you do?

  • A. Create a text dataset on Vertex Al for entity extraction Create as many entities as there are different ingredients and cookware Train an AutoML entity extraction model to extract those entities Evaluate the models performance on a holdout dataset.
  • B. Create a multi-label text classification dataset on Vertex Al Create a test dataset and label each recipe that corresponds to its ingredients and cookware Train a multi-class classification model Evaluate the model's performance on a holdout dataset.
  • C. Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe Evaluate the model's performance on a prelabeled dataset.
  • D. Create a text dataset on Vertex Al for entity extraction Create two entities called ingredient" and cookware" and label at least 200 examples of each entity Train an AutoML entity extraction model to extract occurrences of these entity types Evaluate performance on a holdout dataset.

Answer: D

Explanation:
Entity extraction is a natural language processing (NLP) task that involves identifying and extracting specific types of information from text, such as names, dates, locations, etc. Entity extraction can help you analyze a corpus of recipes and extract each ingredient and cookware mentioned in them. Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides a service for AutoML entity extraction, which allows you to create and train custom entity extraction models without writing any code. You can use Vertex AI to create a text dataset for entity extraction, and label your data with two entities: "ingredient" and "cookware". You need to label at least 200 examples of each entity type to train an AutoML entity extraction model. You can also use a holdout dataset to evaluate the performance of your model, such as precision, recall, and F1-score. This solution can help you build a machine learning model to scan a corpus of recipes and extract each ingredient and cookware mentioned in them, and use the results to help users with meal planning. Reference:
AutoML Entity Extraction | Vertex AI
Preparing data for AutoML Entity Extraction | Vertex AI


NEW QUESTION # 83
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