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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Machine Learning Engineer Certification Exam |
| Exam Number: | Professional-Machine-Learning-Engineer |
| Real Exam Qty: | Approximately 50–60 questions |
| Related Certifications: | Google Cloud Professional Cloud Architect Google Cloud Associate Cloud Engineer Google Cloud Professional Data Engineer |
| Certificate Validity Period: | 2 years |
| Available Languages: | English, Japanese |
| Exam Price: | $200 USD |
| Exam Format: | Multiple select, Case study, Multiple choice |
| Exam Duration: | 120 minutes |
| Recommended Training: | Vertex AI Documentation Google Cloud Skills Boost - Machine Learning Engineer Path |
| Exam Registration: | Kryterion Webassessor Google Cloud Certification Portal |
| Sample Questions: | Google Professional-Machine-Learning-Engineer Sample Questions |
| Exam Way: | Online proctored exam or in-person testing via Kryterion test centers. |
| Pre Condition: | No formal prerequisites required, but 3+ years of industry experience in ML/AI and familiarity with Google Cloud Platform are strongly recommended. |
| Official Syllabus URL: | https://cloud.google.com/certification/machine-learning-engineer |
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The Google Professional Machine Learning Engineer certification exam assesses a candidate's ability to design, build, and optimize machine learning models and systems. Professional-Machine-Learning-Engineer Exam is designed to test a candidate's knowledge of machine learning algorithms, data preprocessing and feature engineering, model selection and training, hyperparameter tuning, and model evaluation and deployment. Professional-Machine-Learning-Engineer exam also focuses on the candidate's ability to work with large-scale datasets, distributed computing systems, and cloud-based machine learning services.
To be eligible for the Google Professional Machine Learning Engineer Certification Exam, you must have a strong background in software engineering, data modeling, and statistics. You must also have hands-on experience working with machine learning frameworks such as TensorFlow or PyTorch, and be familiar with cloud computing platforms such as Google Cloud Platform.
NEW QUESTION # 10
You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set. What should you do?
Answer: D
Explanation:
The test dataset is used to evaluate the performance of the ML model on unseen data. It should reflect the distribution of the data that the model will encounter in production. Therefore, if the retraining data includes new products, the test dataset should also be extended with images of those products to ensure that the model can generalize well to them. Keeping the original test dataset unchanged or replacing it entirely with images of the new products would not capture the diversity of the data that the model needs to handle. Updating the test dataset only when the evaluation metrics drop below a threshold would be reactive rather than proactive, and might result in poor user experience if the model fails to recognize the new products. References:
* Continuous evaluation documentation
* Preparing and using test sets
NEW QUESTION # 11
You are deploying a new version of a model to a production Vertex Al endpoint that is serving traffic You plan to direct all user traffic to the new model You need to deploy the model with minimal disruption to your application What should you do?
Answer: C
NEW QUESTION # 12
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?
Answer: A
NEW QUESTION # 13
You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries:
CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.8);
CREATE OR REPLACE TABLE 'myproject.mydataset.validation' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.2);
After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?
Answer: D
Explanation:
The most likely problem is that the tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table. This is because the RAND() function generates a random number between 0 and 1 for each row, and the probability of a row being in both the training and validation tables is 0.2 * 0.8 = 0.16, which is not negligible. This means that some of the records that you use to validate your model are also used to train your model, which can lead to overfitting and poor generalization. Moreover, the probability of a row being in neither the training nor the validation table is 0.2 *
0.2 = 0.04, which means that you are wasting some of the data in your initial table and reducing the size of your datasets. A better way to split your data into training and validation sets is to use a hash function on a unique identifier column, such as the following queries:
CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS (SELECT * FROM 'myproject.
mydataset.mytable' WHERE MOD(FARM_FINGERPRINT(id), 10) < 8); CREATE OR REPLACE TABLE
'myproject.mydataset.validation' AS (SELECT * FROM 'myproject.mydataset.mytable' WHERE MOD (FARM_FINGERPRINT(id), 10) >= 8); This way, you can ensure that each row has a fixed 80% chance of being in the training table and a 20% chance of being in the validation table, without any overlap or omission.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* BigQuery ML: Splitting data for training and testing
* BigQuery: FARM_FINGERPRINT function
NEW QUESTION # 14
You are working with a dataset that contains customer transactions. You need to build an ML model to predict customer purchase behavior. You plan to develop the model in BigQuery ML, and export it to Cloud Storage for online prediction. You notice that the input data contains a few categorical features, including product category and payment method. You want to deploy the model as quickly as possible. What should you do?
Answer: B
NEW QUESTION # 15
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