PROFESSIONAL-MACHINE-LEARNING-ENGINEER DUMPS COLLECTION & PROFESSIONAL-MACHINE-LEARNING-ENGINEER EXAM DUMP

Professional-Machine-Learning-Engineer Dumps Collection & Professional-Machine-Learning-Engineer Exam Dump

Professional-Machine-Learning-Engineer Dumps Collection & Professional-Machine-Learning-Engineer Exam Dump

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Google Professional Machine Learning Engineer certification is a highly valuable qualification for professionals who are looking to advance their career in the field of machine learning. Google Professional Machine Learning Engineer certification demonstrates that the candidate has the necessary skills and expertise to design, build, and deploy highly scalable and efficient machine learning solutions using Google Cloud's machine learning tools and services. Professional-Machine-Learning-Engineer exam tests the candidate's knowledge of key machine learning concepts, performance-based tasks, and case studies that evaluate the candidate's ability to design and implement machine learning solutions.

Google Professional Machine Learning Engineer certification is a valuable credential for individuals seeking to demonstrate their expertise in machine learning. Professional-Machine-Learning-Engineer Exam covers a wide range of topics and requires candidates to have a solid understanding of machine learning algorithms, statistical analysis, and data visualization. Achieving this certification can help individuals differentiate themselves in the job market and open up new career opportunities.

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The memory needs clues, but also the effective information is connected to systematic study, in order to deepen the learner's impression, avoid the quick forgetting. Therefore, we can see that in the actual Professional-Machine-Learning-Engineer exam questions, how the arrangement plays a crucial role in the teaching effect. The Professional-Machine-Learning-Engineer Study Guide in order to allow the user to form a complete system of knowledge structure, the qualification Professional-Machine-Learning-Engineer examination of test interpretation and supporting course practice organic reasonable arrangement together.

Google Professional Machine Learning Engineer exam requires hands-on experience with the following while also including advanced knowledge of machine learning and expertise in designing and implementing appropriate ML architectures: Data preprocessing, Feature engineering, Model building, Model deployment, Model monitoring, Outlier detection, Hyperparameter tuning, and Algorithm selection. The Google Professional Machine Learning Engineer certification aims to authenticate these expertise areas along with practical experience to validate oneself as a versatile, employable programming professional.

Google Professional Machine Learning Engineer Sample Questions (Q41-Q46):

NEW QUESTION # 41
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

  • A. Deploy and version the model on Al Platform.
  • B. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.
  • C. Use Dataflow with the SavedModel to read the data from BigQuery
  • D. Export the model to BigQuery ML.

Answer: D

Explanation:
https://cloud.google.com/bigquery-ml/docs/making-predictions-with-imported-tensorflow-models
https://cloud.google.com/bigquery-ml/docs/making-predictions-with-imported-tensorflow-models#importing_models
https://cloud.google.com/bigquery-ml/docs/making-predictions-with-imported-tensorflow-models#bq CREATE OR REPLACE MODEL example_dataset.imported_tf_model OPTIONS (MODEL_TYPE='TENSORFLOW', MODEL_PATH='gs://cloud-training-demos/txtclass/export/exporter/1549825580/*')


NEW QUESTION # 42
You recently created a new Google Cloud Project After testing that you can submit a Vertex Al Pipeline job from the Cloud Shell, you want to use a Vertex Al Workbench user-managed notebook instance to run your code from that instance You created the instance and ran the code but this time the job fails with an insufficient permissions error. What should you do?

  • A. Ensure that the Vertex Al Workbench instance is assigned the Identity and Access Management (1AM) Notebooks Runner role.
  • B. Ensure that the Vertex Al Workbench instance is assigned the Identity and Access Management (1AM) Vertex Al User rote.
  • C. Ensure that the Workbench instance that you created is in the same region of the Vertex Al Pipelines resources you will use.
  • D. Ensure that the Vertex Al Workbench instance is on the same subnetwork of the Vertex Al Pipeline resources that you will use.

Answer: B

Explanation:
Vertex AI Workbench is an integrated development environment (IDE) that allows you to create and run Jupyter notebooks on Google Cloud. Vertex AI Pipelines is a service that allows you to create and manage machine learning workflows using Vertex AI components. To submit a Vertex AI Pipeline job from a Vertex AI Workbench instance, you need to have the appropriate permissions to access the Vertex AI resources. The Identity and Access Management (IAM) Vertex AI User role is a predefined role that grants the minimum permissions required to use Vertex AI services, such as creating and deploying models, endpoints, and pipelines. By assigning the Vertex AI User role to the Vertex AI Workbench instance, you can ensure that the instance has sufficient permissions to submit a Vertex AI Pipeline job. You can assign the role to the instance by using the Cloud Console, the gcloud command-line tool, or the Cloud IAM API. Reference: The answer can be verified from official Google Cloud documentation and resources related to Vertex AI Workbench, Vertex AI Pipelines, and IAM.
Vertex AI Workbench | Google Cloud
Vertex AI Pipelines | Google Cloud
Vertex AI roles | Google Cloud
Granting, changing, and revoking access to resources | Google Cloud


NEW QUESTION # 43
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 Memorystore for writing and for reading the user's navigation context, and then deploy the model on Google Kubernetes Engine.
  • B. 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.
  • C. Embed the client on the website, and then deploy the model on AI Platform Prediction.
  • D. Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.

Answer: D


NEW QUESTION # 44
You need to develop an image classification model by using a large dataset that contains labeled images in a Cloud Storage Bucket. What should you do?

  • A. Use Vertex Al Pipelines with TensorFlow Extended (TFX) to create a pipeline that reads the images from Cloud Storage and trams the model.
  • B. Convert the image dataset to a tabular format using Dataflow Load the data into BigQuery and use BigQuery ML to tram the model.
  • C. Use Vertex Al Pipelines with the Kubeflow Pipelines SDK to create a pipeline that reads the images from Cloud Storage and trains the model.
  • D. Import the labeled images as a managed dataset in Vertex Al: and use AutoML to tram the model.

Answer: D

Explanation:
The best option for developing an image classification model by using a large dataset that contains labeled images in a Cloud Storage bucket is to import the labeled images as a managed dataset in Vertex AI and use AutoML to train the model. This option allows you to leverage the power and simplicity of Google Cloud to create and deploy a high-quality image classification model with minimal code and configuration. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can create a managed dataset from a Cloud Storage bucket that contains labeled images, which can be used to train an AutoML model. AutoML is a service that can automatically build and optimize machine learning models for various tasks, such as image classification, object detection, natural language processing, and tabular data analysis. AutoML can handle the complex aspects of machine learning, such as feature engineering, model architecture, hyperparameter tuning, and model evaluation. AutoML can also evaluate, deploy, and monitor the image classification model, and provide online or batch predictions. By using Vertex AI and AutoML, users can develop an image classification model by using a large dataset with ease and efficiency.
The other options are not as good as option C, for the following reasons:
* Option A: Using Vertex AI Pipelines with the Kubeflow Pipelines SDK to create a pipeline that reads the images from Cloud Storage and trains the model would require more skills and steps than using Vertex AI and AutoML. Vertex AI Pipelines is a service that can orchestrate machine learning workflows using Vertex AI. Vertex AI Pipelines can run preprocessing and training steps on custom Docker images, and evaluate, deploy, and monitor the machine learning model. Kubeflow Pipelines SDK is a Python library that can create and run pipelines on Vertex AI Pipelines or on Kubeflow, an open-source platform for machine learning on Kubernetes. However, using Vertex AI Pipelines and Kubeflow Pipelines SDK would require writing code, building Docker images, defining pipeline components and steps, and managing the pipeline execution and artifacts. Moreover, Vertex AI Pipelines and Kubeflow Pipelines SDK are not specialized for image classification, and users would need to use other libraries or frameworks, such as TensorFlow or PyTorch, to build and train the image classification model.
* Option B: Using Vertex AI Pipelines with TensorFlow Extended (TFX) to create a pipeline that reads the images from Cloud Storage and trains the model would require more skills and steps than using Vertex AI and AutoML. TensorFlow Extended (TFX) is a framework that can create and run end-to-end machine learning pipelines on TensorFlow, a popular library for building and training deep learning models. TFX can preprocess the data, train and evaluate the model, validate and push the model, and serve the model for online or batch predictions. However, using Vertex AI Pipelines and TFX would require writing code, building Docker images, defining pipeline components and steps, and managing the pipeline execution and artifacts. Moreover, TFX is not optimized for image classification, and users would need to use other libraries or tools, such as TensorFlow Data Validation, TensorFlow Transform, and TensorFlow Hub, to handle the image data and the model architecture.
* Option D: Converting the image dataset to a tabular format using Dataflow, loading the data into BigQuery, and using BigQuery ML to train the model would not handle the image data properly and could result in a poor model performance. Dataflow is a service that can create scalable and reliable pipelines to process large volumes of data from various sources. Dataflow can preprocess the data by using Apache Beam, a programming model for defining and executing data processing workflows.
BigQuery is a serverless, scalable, and cost-effective data warehouse that can perform fast and interactive queries on large datasets. BigQuery ML is a service that can create and train machine learning models by using SQL queries on BigQuery. However, converting the image data to a tabular format would lose the spatial and semantic information of the images, which are essential for image classification. Moreover, BigQuery ML is not specialized for image classification, and users would need to use other tools or techniques, such as feature hashing, embedding, or one-hot encoding, to handle the categorical features.


NEW QUESTION # 45
Your company stores a large number of audio files of phone calls made to your customer call center in an on-premises database. Each audio file is in wav format and is approximately 5 minutes long. You need to analyze these audio files for customer sentiment. You plan to use the Speech-to-Text API. You want to use the most efficient approach. What should you do?

  • A. 1 Upload the audio files to Cloud Storage
    2. Call the speech: Iongrunningrecognize API endpoint to generate transcriptions
    3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions
  • B. 1 Iterate over your local files in Python
    2 Use the Speech-to-Text Python Library to create a speech.RecognitionAudio object, and set the content to the audio file data
    3. Call the speech: lengrunningrecognize API endpoint to generate transcriptions
    4 Call the Natural Language API by using the analyzesenriment method
  • C. 1 Upload the audio files to Cloud Storage
    2 Call the speech: Iongrunningrecognize API endpoint to generate transcriptions.
    3 Create a Cloud Function that calls the Natural Language API by using the analyzesentiment method
  • D. 1 Iterate over your local Tiles in Python
    2. Use the Speech-to-Text Python library to create a speech.RecognitionAudio object and set the content to the audio file data
    3. Call the speech: recognize API endpoint to generate transcriptions
    4. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions

Answer: C

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "design, build, and productionalize ML models to solve business challenges using Google Cloud technologies". The Speech-to-Text API2 allows you to convert audio to text by applying powerful neural network models. The Natural Language API3 enables you to analyze text and extract information about the sentiment, entities, and syntax. The Cloud Functions4 service lets you write and deploy code that runs in response to events, such as a Pub/Sub message or an HTTP request. Therefore, option B is the most efficient approach to analyze the audio files for customer sentiment, as it leverages the existing Google Cloud services and avoids unnecessary data processing and model training. The other options are not relevant or optimal for this scenario. References:
* Professional ML Engineer Exam Guide
* Speech-to-Text API
* Natural Language API
* Cloud Functions
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions


NEW QUESTION # 46
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