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Google Professional Machine Learning Engineer certification is highly regarded in the industry and is recognized as a benchmark for machine learning expertise. It is an ideal certification for professionals who are looking to enhance their career prospects and advance their skills in machine learning. Google Professional Machine Learning Engineer certification demonstrates that the candidate has the necessary skills to design and implement machine learning solutions that meet the requirements of modern businesses.

The Google Professional-Machine-Learning-Engineer exam consists of multiple-choice questions and coding exercises that test a candidate's knowledge and practical skills in machine learning development, data preparation, model training, and deployment. Professional-Machine-Learning-Engineer exam is three hours long and is held at a proctored testing center, or online for an additional fee.

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Google Professional Machine Learning Engineer Certification Exam has become increasingly popular among individuals who are looking to enhance their career in machine learning engineering. Google Professional Machine Learning Engineer certification is recognized globally and is highly valued by employers. It helps professionals to develop their skills and knowledge in the field of machine learning engineering and provides them with an opportunity to showcase their expertise to potential employers. With the increasing demand for machine learning engineers, obtaining this certification can help individuals to stand out in the competitive job market.

Google Professional Machine Learning Engineer Sample Questions (Q111-Q116):

NEW QUESTION # 111
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?

A. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
B. Reduce the learning rate and run the training process until the training loss stops decreasing.
C. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
D. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.

Answer: B

NEW QUESTION # 112
You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company's weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter's published date and the user remains on the page for at least one minute.
All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model's performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?

A. Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.
B. Schedule a daily Dataflow job in Cloud Composer to compute the success metric.
C. Schedule a weekly query in BigQuery to compute the success metric.
D. Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.

Answer: C

Explanation:
Scheduling a weekly query in BigQuery to compute the success metric is a cost-effective way to monitor the model's performance. BigQuery allows you to run complex queries on large datasets in a cost-effective and performant manner. By using BigQuery, you can compute the success metric on a regular basis without incurring the additional costs of other services such as Vertex AI or Cloud Composer.
Additionally, by scheduling the query to run weekly, you can ensure that you are monitoring the model's performance in a timely manner, while still providing enough time for the model to degrade below the acceptable baseline. You can then use the results of the query to determine when retraining is necessary.

NEW QUESTION # 113
You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (Pll) to Google Cloud. You want to use the Cloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the Pll is not accessible by unauthorized individuals?

A. Create two buckets of data Sensitive and Non-sensitive Write all data to the Non-sensitive bucket Periodically conduct a bulk scan of that bucket using the DLP API, and move the sensitive data to the Sensitive bucket
B. Stream all files to Google CloudT and then write the data to BigQuery Periodically conduct a bulk scan of the table using the DLP API.
C. Stream all files to Google Cloud, and write batches of the data to BigQuery While the data is being written to BigQuery conduct a bulk scan of the data using the DLP API.
D. Create three buckets of data: Quarantine, Sensitive, and Non-sensitive Write all data to the Quarantine bucket. Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket

Answer: B

NEW QUESTION # 114
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:
New Professional-Machine-Learning-Engineer Dumps Pdf, Professional-Machine-Learning-Engineer Trustworthy Practice
A)

B)

C)

D)

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

Answer: B

NEW QUESTION # 115
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?

A. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
B. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
C. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
D. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run

Answer: B

Explanation:
https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#cicd_architecture

NEW QUESTION # 116
......

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New Professional-Machine-Learning-Engineer Dumps Pdf, Professional-Machine-Learning-Engineer Trustworthy Practice

New Professional-Machine-Learning-Engineer Dumps Pdf - Latest Google Professional-Machine-Learning-Engineer Trustworthy Practice: Google Professional Machine Learning Engineer

Google Professional Machine Learning Engineer Sample Questions (Q111-Q116):

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