Exam Google Professional-Machine-Learning-Engineer Quizzes & Current Professional-Machine-Learning-Engineer Exam Content

We provide three versions to let the clients choose the most suitable equipment on their hands to learn the Professional-Machine-Learning-Engineer study materials such as the smart phones, the laptops and the tablet computers. We provide the professional staff to reply your problems about our study materials online in the whole day and the timely and periodical update to the clients. So you will definitely feel it is your fortune to buy our Professional-Machine-Learning-Engineer Study Materials.

Google Professional Machine Learning Engineer Certification Exam is a highly sought-after certification that demonstrates a professional's proficiency in designing and deploying machine learning models on Google Cloud Platform. Google Professional Machine Learning Engineer certification is designed for professionals who have experience in machine learning and are looking to advance their career in this field. Professional-Machine-Learning-Engineer Exam is designed to test the candidate's knowledge and skills in various aspects of machine learning, such as data preparation, model training, model evaluation, and optimization.

>> Exam Google Professional-Machine-Learning-Engineer Quizzes <<

Current Google Professional-Machine-Learning-Engineer Exam Content, Valid Professional-Machine-Learning-Engineer Test Pattern

Our Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) prep material also includes web-based and desktop Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) practice tests for you to put your skills to the test. Our Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) practice exams simulate the real Prepare for your Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) exam environment, so you can experience the pressure and environment of the actual test before the day arrives. You'll receive detailed feedback on your performance, so you know what areas to focus on and improve.

Google Professional Machine Learning Engineer Sample Questions (Q82-Q87):

NEW QUESTION # 82
You work at a large organization that recently decided to move their ML and data workloads to Google Cloud.
The data engineering team has exported the structured data to a Cloud Storage bucket in Avro format. You need to propose a workflow that performs analytics, creates features, and hosts the features that your ML models use for online prediction How should you configure the pipeline?

A. Ingest the Avro files into Cloud Spanner to perform analytics Use a Dataflow pipeline to create the features and store them in BigQuery for online prediction.
B. Ingest the Avro files into BigQuery to perform analytics Use a Dataflow pipeline to create the features, and store them in Vertex Al Feature Store for online prediction.
C. Ingest the Avro files into Cloud Spanner to perform analytics. Use a Dataflow pipeline to create the features. and store them in Vertex Al Feature Store for online prediction.
D. Ingest the Avro files into BigQuery to perform analytics Use BigQuery SQL to create features and store them in a separate BigQuery table for online prediction.

Answer: B

Explanation:
BigQuery is a service that allows you to store and query large amounts of data in a scalable and cost-effective way. You can use BigQuery to ingest the Avro files from the Cloud Storage bucket and perform analytics on the structured data. Avro is a binary file format that can store complex data types and schemas. You can use the bq load command or the BigQuery API to load the Avro files into a BigQuery table. You can then use SQL queries to analyze the data and generate insights. Dataflow is a service that allows you to create and run scalable and portable data processing pipelines on Google Cloud. You can use Dataflow to create the features for your ML models, such as transforming, aggregating, and encoding the data. You can use the Apache Beam SDK to write your Dataflow pipeline code in Python or Java. You can also use the built-in transforms or custom transforms to apply the feature engineering logic to your data. Vertex AI Feature Store is a service that allows you to store and manage your ML features on Google Cloud. You can use Vertex AI Feature Store to host the features that your ML models use for online prediction. Online prediction is a type of prediction that provides low-latency responses to individual or small batches of input data. You can use the Vertex AI Feature Store API to write the features from your Dataflow pipeline to a feature store entity type. You can then use the Vertex AI Feature Store online serving API to read the features from the feature store and pass them to your ML models for online prediction. By using BigQuery, Dataflow, and Vertex AI Feature Store, you can configure a pipeline that performs analytics, creates features, and hosts the features that your ML models use for online prediction. References:
* BigQuery documentation
* Dataflow documentation
* Vertex AI Feature Store documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 83
You want to migrate a scikrt-learn classifier model to TensorFlow. You plan to train the TensorFlow classifier model using the same training set that was used to train the scikit-learn model and then compare the performances using a common test set. You want to use the Vertex Al Python SDK to manually log the evaluation metrics of each model and compare them based on their F1 scores and confusion matrices. How should you log the metrics?

A. Option C
B. Option D
C. Option B
D. Option A

Answer: B

Explanation:
To log the metrics of a machine learning model in TensorFlow using the Vertex AI Python SDK, you should utilize the aiplatform.log_metrics function to log the F1 score and aiplatform.log_classification_metrics function to log the confusion matrix. These functions allow users to manually record and store evaluation metrics for each model, facilitating an efficient comparison based on specific performance indicators like F1 scores and confusion matrices. Reference: The answer can be verified from official Google Cloud documentation and resources related to Vertex AI and TensorFlow.
Vertex AI Python SDK reference | Google Cloud
Logging custom metrics | Vertex AI
Migrating from scikit-learn to TensorFlow | TensorFlow

NEW QUESTION # 84
Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?

A. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
B. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
C. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
D. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.

Answer: A

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html

NEW QUESTION # 85
You work at a gaming startup that has several terabytes of structured data in Cloud Storage. This data includes gameplay time data, user metadata, and game metadata. You want to build a model that recommends new games to users that requires the least amount of coding. What should you do?

A. Load the data in BigQuery. Use BigQuery ML to train an Autoencoder model.
B. Read data to a Vertex Al Workbench notebook. Use TensorFlow to train a two-tower model.
C. Load the data in BigQuery. Use BigQuery ML to train a matrix factorization model.
D. Read data to a Vertex Al Workbench notebook. Use TensorFlow to train a matrix factorization model.

Answer: C

Explanation:
The best option to build a game recommendation model with the least amount of coding is to use BigQuery ML, which allows you to create and execute machine learning models using standard SQL queries. BigQuery ML supports several types of models, including matrix factorization, which is a common technique for collaborative filtering-based recommendation systems. Matrix factorization models learn latent factors for users and items from the observed ratings, and then use them to predict the ratings for new user-item pairs.
BigQuery ML provides a built-in function called ML.RECOMMEND that can generate recommendations for a given user based on a trained matrix factorization model. To use BigQuery ML, you need to load the data in BigQuery, which is a serverless, scalable, and cost-effective data warehouse. You can use the bq command- line tool, the BigQuery API, or the Cloud Console to load data from Cloud Storage to BigQuery.
Alternatively, you can use federated queries to query data directly from Cloud Storage without loading it to BigQuery, but this may incur additional costs and performance overhead. Option A is incorrect because BigQuery ML does not support Autoencoder models, which are a type of neural network that can learn compressed representations of the input data. Autoencoder models are not suitable for recommendation systems, as they do not capture the interactions between users and items. Option C is incorrect because using TensorFlow to train a two-tower model requires more coding than using BigQuery ML. A two-tower model is a type of neural network that learns embeddings for users and items separately, and then combines them with a dot product or a cosine similarity to compute the rating. TensorFlow is a low-level framework that requires you to define the model architecture, the loss function, the optimizer, the training loop, and the evaluation metrics. Moreover, you need to read the data from Cloud Storage to a Vertex AI Workbench notebook, which is an instance of JupyterLab that runs on a Google Cloud virtual machine. This may involve additional steps such as authentication, authorization, and data preprocessing. Option D is incorrect because using TensorFlow to train a matrix factorization model also requires more coding than using BigQuery ML. Although TensorFlow provides some high-level APIs such as Keras and TensorFlow Recommenders that can simplify the model development, you still need to handle the data loading and the model training and evaluation yourself. Furthermore, you need to read the data from Cloud Storage to a Vertex AI Workbench notebook, which may incur additional complexity and costs. References:
* BigQuery ML documentation
* Using matrix factorization with BigQuery ML
* Recommendations AI documentation
* Loading data into BigQuery
* Querying data in Cloud Storage from BigQuery
* Vertex AI Workbench documentation
* TensorFlow documentation
* TensorFlow Recommenders documentation

NEW QUESTION # 86
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

A. Three individual features binned latitude, binned longitude, and one-hot encoded car type
B. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type
C. One feature obtained as an element-wise product between latitude, longitude, and car type
D. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type

Answer: D

Explanation:
A feature cross is a synthetic feature that is obtained by combining two or more existing features, usually by taking their product or concatenation. A feature cross can help to capture the nonlinear and interaction effects between the original features, and improve the predictive performance of themodel. A feature cross can be applied to different types of features, such as numeric, categorical, or geospatial features1.
For the use case of building an ML model to predict car sales in different cities around the world, the best option is to use one feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type. This option involves creating a feature cross that combines three individual features: binned latitude, binned longitude, and one-hot encoded car type. Binning is a technique that transforms a continuous numeric feature into a discrete categorical feature by dividing its range into equal intervals, or bins. One-hot encoding is a technique that transforms a categorical feature into a binary vector, where each element corresponds to a possible category, and has a value of 1 if the feature belongs to that category, and 0 otherwise. By applying binning and one-hot encoding to the latitude, longitude, and car type features, the feature cross can capture the city-specific relationships between car type and number of sales, as each combination of bins and car types can represent a different city and its preference for a certain car type.
For example, the feature cross can learn that a city with a latitude bin of [40, 50], a longitude bin of [-80, -70], and a car type of SUV has a higher number of sales than a city with a latitude bin of [-10, 0], a longitude bin of
[10, 20], and a car type of sedan. Therefore, using one feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type is the best option for this use case.
References:
* Feature Crosses | Machine Learning Crash Course

NEW QUESTION # 87
......

With “reliable credit” as the soul of our Professional-Machine-Learning-Engineer study tool, “utmost service consciousness” as the management philosophy, we endeavor to provide customers with high quality service. Our customer service staff, who are willing to be your little helper and answer your any questions about our Google Professional Machine Learning Engineer qualification test, fully implement the service principle of customer-oriented service activities, aiming at comprehensive, coordinated and sustainable cooperation relationship with every users. Any puzzle about our Professional-Machine-Learning-Engineer Test Torrent will receive timely and effective response, just leave a message on our official website or send us an e-mail at your convenience.

Current Professional-Machine-Learning-Engineer Exam Content: https://www.dumpsmaterials.com/Professional-Machine-Learning-Engineer-real-torrent.html

0

Course Enrolled

0

Course Completed

Zack Brown Zack Brown

About me

Exam Google Professional-Machine-Learning-Engineer Quizzes & Current Professional-Machine-Learning-Engineer Exam Content

Current Google Professional-Machine-Learning-Engineer Exam Content, Valid Professional-Machine-Learning-Engineer Test Pattern

Google Professional Machine Learning Engineer Sample Questions (Q82-Q87):

0

0

Sign in