Free PDF 2025 High-quality Google Professional-Machine-Learning-Engineer: Google Professional Machine Learning Engineer Test Sample Questions

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To obtain the Google Professional Machine Learning Engineer certification, candidates must pass a 2-hour exam that consists of multiple-choice and scenario-based questions. Professional-Machine-Learning-Engineer exam evaluates the candidates on their ability to design and develop scalable, efficient, and secure machine learning models using Google Cloud Platform. Google Professional Machine Learning Engineer certification is recognized globally and is highly valued by employers as it demonstrates a professional's expertise and ability to work with cutting-edge technologies in the field of machine learning. Google Professional Machine Learning Engineer certification also provides access to Google's resources and community of machine learning professionals, making it a valuable asset for anyone looking to advance their career in this field.

Google Professional Machine Learning Engineer exam is designed to test the expertise of individuals in the field of machine learning. Google Professional Machine Learning Engineer certification provides a strong foundation in machine learning concepts and tools, as well as the ability to develop and deploy sophisticated machine learning models using Google Cloud technologies. Professional-Machine-Learning-Engineer Exam assesses one's ability to use Google’s machine learning tools and services to build and deploy robust, scalable, and efficient machine learning models.

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Google Professional Machine Learning Engineer Sample Questions (Q166-Q171):

NEW QUESTION # 166
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

A. Kubeflow Pipelines and Al Platform Prediction
B. Cloud Composer, Al Platform Training with custom containers , and App Engine
C. Cloud Composer, BigQuery ML , and Al Platform Prediction
D. Kubeflow Pipelines and App Engine

Answer: A

NEW QUESTION # 167
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
* Real-time analytics
* Interactive analytics of historical data
* Clickstream analytics
* Product recommendations
Which services should the Specialist use?

A. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations
B. Amazon Athena as the data catalog: Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-real-time data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
C. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
D. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real- time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations

Answer: D

NEW QUESTION # 168
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 For example, suppose you are training a model with purchase data from a number of stores. You know, however, that the model will be used primarily to make predictions for stores that are not in the training data. To ensure that the model can generalize to unseen stores, you should segregate your data sets by stores. In other words, your test set should include only stores different from the evaluation set, and the evaluation set should include only stores different from the training set. https://cloud.google.com/automl-tables/docs/prepare#ml-use

NEW QUESTION # 169
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. 500*256*0 25+256*128*0 25+128*2 = 40448
B. 500*256+256*128+128*2 = 161024
C. 501*256+257*128+128*2=161408
D. 501*256+257*128+2 = 161154

Answer: A

NEW QUESTION # 170
You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?

A. Create a Vertex Al Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.
B. Create a Vertex Al Workbench managed notebook on a Dataproc cluster, and use the spark-bigquery- connector to access the tables.
C. Create a Vertex Al Workbench user-managed notebook using the default VM instance, and use the %% bigquery magic commands in Jupyter to query the tables.
D. Create a Vertex Al Workbench user-managed notebook on a Dataproc Hub. and use the %%bigquery magic commands in Jupyter to query the tables.

Answer: C

Explanation:
* Cost-effectiveness: User-managed notebooks in Vertex AI Workbench allow you to leverage pre- configured virtual machines with reasonable resource allocation, keeping costs lower compared to options involving managed notebooks or Dataproc clusters.
* Development flexibility: User-managed notebooks offer full control over the environment, allowing you to install additional libraries or dependencies needed for your specific EDA, preprocessing, and model training tasks. This flexibility is crucial while experimenting with different algorithms.
* BigQuery integration: The %%bigquery magic commands provide seamless integration with BigQuery within the Jupyter Notebook environment. This enables efficient querying and exploration of customer transaction data stored in BigQuery directly from the notebook, streamlining the workflow.
Other options and why they are not the best fit:
* B. Managed notebook: While managed notebooks offer an easier setup, they might have limited customization options, potentially hindering your ability to install specific libraries or tools.
* C. Dataproc Hub: Dataproc Hub focuses on running large-scale distributed workloads, and it might be overkill for your scenario involving exploratory analysis and experimentation with different algorithms.
Additionally, it could incur higher costs compared to a user-managed notebook.
* D. Dataproc cluster with spark-bigquery-connector: Similar to option C, using a Dataproc cluster with the spark-bigquery-connector would be more complex and potentially more expensive than using %% bigquery magic commands within a user-managed notebook for accessing BigQuery data.
References:
* https://cloud.google.com/vertex-ai/docs/workbench/instances/bigquery
* https://cloud.google.com/vertex-ai-notebooks

NEW QUESTION # 171
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FREE PDF 2025 HIGH-QUALITY GOOGLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER: GOOGLE PROFESSIONAL MACHINE LEARNING ENGINEER TEST SAMPLE QUESTIONS

Free PDF 2025 High-quality Google Professional-Machine-Learning-Engineer: Google Professional Machine Learning Engineer Test Sample Questions