BRAINDUMPS GOOGLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER DOWNLOADS, PROFESSIONAL-MACHINE-LEARNING-ENGINEER FREE SAMPLE QUESTIONS

Braindumps Google Professional-Machine-Learning-Engineer Downloads, Professional-Machine-Learning-Engineer Free Sample Questions

Braindumps Google Professional-Machine-Learning-Engineer Downloads, Professional-Machine-Learning-Engineer Free Sample Questions

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Google Professional Machine Learning Engineer exam is a certification test that is designed to validate the skills and knowledge of individuals in the field of machine learning. Professional-Machine-Learning-Engineer Exam is intended for individuals who have a strong understanding of machine learning concepts, including supervised learning, unsupervised learning, and deep learning. Additionally, this certification exam assesses an individual's ability to design and implement machine learning models on the Google Cloud Platform.

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.

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Achieving the Google Professional Machine Learning Engineer certification is a significant accomplishment for any individual seeking to validate their expertise in machine learning. Google Professional Machine Learning Engineer certification demonstrates that an individual has the skills and knowledge required to design, build, and deploy machine learning models on Google Cloud Platform. It also enables individuals to differentiate themselves in the job market and opens up new career opportunities.

Google Professional Machine Learning Engineer Sample Questions (Q137-Q142):

NEW QUESTION # 137
You work at an organization that maintains a cloud-based communication platform that integrates conventional chat, voice, and video conferencing into one platform. The audio recordings are stored in Cloud Storage. All recordings have an 8 kHz sample rate and are more than one minute long. You need to implement a new feature in the platform that will automatically transcribe voice call recordings into a text for future applications, such as call summarization and sentiment analysis. How should you implement the voice call transcription feature following Google-recommended best practices?

  • A. Upsample the audio recordings to 16 kHz. and transcribe the audio by using the Speech-to-Text API with synchronous recognition.
  • B. Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.
  • C. Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with synchronous recognition.
  • D. Upsample the audio recordings to 16 kHz. and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.

Answer: D


NEW QUESTION # 138
You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

  • A. Execute a query in BigQuery to retrieve all the existing table names in your project using the
  • B. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
  • C. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.
  • D. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.

Answer: C

Explanation:
INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.
Explanation:
A should be the way to go for large datasets --This is also good but it is legacy way of checking:- NFORMATION_SCHEMA contains these views for table metadata: TABLES and TABLE_OPTIONS for metadata about tables. COLUMNS and COLUMN_FIELD_PATHS for metadata about columns and fields. PARTITIONS for metadata about table partitions (Preview)


NEW QUESTION # 139
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

  • A. Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately Choose an automatic data split across the training, validation, and testing sets
  • B. Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate transformations Choose an automatic data split across the training, validation, and testing sets
  • C. Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
  • D. Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set

Answer: D


NEW QUESTION # 140
You developed a custom model by using Vertex Al to predict your application's user churn rate You are using Vertex Al Model Monitoring for skew detection The training data stored in BigQuery contains two sets of features - demographic and behavioral You later discover that two separate models trained on each set perform better than the original model You need to configure a new model mentioning pipeline that splits traffic among the two models You want to use the same prediction-sampling-rate and monitoring-frequency for each model You also want to minimize management effort What should you do?

  • A. Separate the training dataset into two tables based on demographic and behavioral features Deploy the models to two separate endpoints, and submit two Vertex Al Model Monitoring jobs
  • B. Keep the training dataset as is Deploy the models to two separate endpoints and submit two Vertex Al Model Monitoring jobs with appropriately selected feature-thresholds parameters
  • C. Keep the training dataset as is Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and feature selections
  • D. Separate the training dataset into two tables based on demographic and behavioral features. Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets

Answer: C


NEW QUESTION # 141
You are developing a model to help your company create more targeted online advertising campaigns. You need to create a dataset that you will use to train the model. You want to avoid creating or reinforcing unfair bias in the model. What should you do?
Choose 2 answers

  • A. Conduct fairness tests across sensitive categories and demographics on the trained model.
  • B. Collect a stratified sample of production traffic to build the training dataset.
  • C. include only the demographic groups that most frequently interact with advertisements.
  • D. Include a comprehensive set of demographic features.
  • E. Collect a random sample of production traffic to build the training dataset.

Answer: B,E


NEW QUESTION # 142
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