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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

Section	Objectives
Topic 1: Deployment and operations	- Monitoring and maintenance 1. Monitor model drift and performance 2. Retraining and lifecycle management - Model deployment 1. Deploy models using Vertex AI endpoints 2. Batch and online prediction systems
Topic 2: Data preparation and processing	- Feature engineering 1. Transform and preprocess datasets 2. Feature selection and representation techniques - Data ingestion and pipelines 1. Use BigQuery and data processing services 2. Build data pipelines for training and serving
Topic 3: ML pipeline automation and orchestration	- Pipeline design 1. Build end-to-end ML pipelines 2. Use Vertex AI Pipelines
Topic 4: ML model development	- Model training and tuning 1. Hyperparameter tuning and optimization 2. Train models using TensorFlow / Vertex AI - Evaluation 1. Evaluate model performance metrics 2. Model validation strategies
Topic 5: Designing ML solutions	- ML architecture design 1. Select appropriate ML models and approaches 2. Design scalable ML systems on GCP - Framing ML problems 1. Define success metrics and evaluation criteria 2. Translate business problems into ML tasks

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Google Professional Machine Learning Engineer Sample Questions (Q149-Q154):

NEW QUESTION # 149
You work for a retail company. You have created a Vertex AI forecast model that produces monthly item sales predictions. You want to quickly create a report that will help to explain how the model calculates the predictions. You have one month of recent actual sales data that was not included in the training dataset. How should you generate data for your report?

A. Create a batch prediction job by using the actual sales data. Compare the predictions to the actuals in the report.
B. Generate counterfactual examples by using the actual sales data. Create a batch prediction job using the actual sales data and the counterfactual examples. Compare the results in the report.
C. Create a batch prediction job by using the actual sales data, and configure the job settings to generate feature attributions. Compare the results in the report.
D. Train another model by using the same training dataset as the original, and exclude some columns. Using the actual sales data create one batch prediction job by using the new model and another one with the original model. Compare the two sets of predictions in the report.

Answer: C

NEW QUESTION # 150
You are analyzing customer data for a healthcare organization that is stored in Cloud Storage.
The data contains personally identifiable information (PII). You need to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields. What should you do?

A. Use customer-managed encryption keys (CMEK) to encrypt the PII data at rest, and decrypt the PII data during data exploration and preprocessing.
B. Use Google-managed encryption keys to encrypt the PII data at rest, and decrypt the PII data during data exploration and preprocessing.
C. Use a VM inside a VPC Service Controls security perimeter to perform data exploration and preprocessing.
D. Use the Cloud Data Loss Prevention (DLP) API to de-identify the PII before performing data exploration and preprocessing.

Answer: D

NEW QUESTION # 151
Your work for a textile manufacturing company. Your company has hundreds of machines and each machine has many sensors. Your team used the sensory data to build hundreds of ML models that detect machine anomalies Models are retrained daily and you need to deploy these models in a cost-effective way. The models must operate 24/7 without downtime and make sub millisecond predictions. What should you do?

A. Deploy a Dataflow batch pipeline and a Vertex Al Prediction endpoint.
B. Deploy a Dataflow streaming pipeline and a Vertex Al Prediction endpoint with autoscaling.
C. Deploy a Dataflow batch pipeline with the Runlnference API. and use model refresh.
D. Deploy a Dataflow streaming pipeline with the Runlnference API and use automatic model refresh.

Answer: D

Explanation:
A Dataflow streaming pipeline is a cost-effective way to process large volumes of real-time data from sensors. The RunInference API is a Dataflow transform that allows you to run online predictions on your streaming data using your ML models. By using the RunInference API, you can avoid the latency and cost of using a separate prediction service. The automatic model refresh feature enables you to update your models in the pipeline without redeploying the pipeline. This way, you can ensure that your models are always up-to- date and accurate. By deploying a Dataflow streaming pipeline with the RunInference API and using automatic model refresh, you can achieve sub-millisecond predictions, 24/7 availability, and low operational overhead for your ML models. References:
* Dataflow documentation
* RunInference API documentation
* Automatic model refresh documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 152
You are working on a prototype of a text classification model in a managed Vertex AI Workbench notebook. You want to quickly experiment with tokenizing text by using a Natural Language Toolkit (NLTK) library. How should you add the library to your Jupyter kernel?

A. Write a custom Dataflow job that uses NLTK to tokenize your text and saves the output to Cloud Storage.
B. Install the NLTK library from a Jupyter cell by using the! pip install nltk -user command.
C. Install the NLTK library from a terminal by using the pip install nltk command.
D. Create a new Vertex Al Workbench notebook with a custom image that includes the NLTK library.

Answer: B

Explanation:
NLTK is a Python library that provides a set of tools for natural language processing, such as tokenization, stemming, tagging, parsing, and sentiment analysis. Tokenization is a process of breaking a text into smaller units, such as words or sentences. You can use NLTK to quickly experiment with tokenizing text in a managed Vertex AI Workbench notebook. A Vertex AI Workbench notebook is a web-based interactive environment that allows you to write and execute Python code on Google Cloud. You can install the NLTK library from a Jupyter cell by using the !pip install nltk --user command. This command uses the pip package manager to install the NLTK library for the current user. By installing the NLTK library from a Jupyter cell, you can avoid the hassle of opening a terminal or creating a custom image for your notebook. Reference:
NLTK documentation
Vertex AI Workbench documentation
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 153
You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company's logo. In the dataset, 96% of examples don't have the logo, so the dataset is very skewed. Which metrics would give you the most confidence in your model?

A. F-score where recall is weighed more than precision
B. F-score where precision is weighed more than recall
C. F1 score
D. RMSE

Answer: A

Explanation:
* Option A is correct because using F-score where recall is weighed more than precision is a suitable metric for binary classification with imbalanced data. F-score is a harmonic mean of precision and recall, which are two metrics that measure the accuracy and completeness of the positive class1. Precision is the fraction of true positives among all predicted positives, while recall is the fraction of true positives among all actual positives1. When the data is imbalanced, the positive class is the minority class, which is usually the class of interest. For example, in this case, the positive class is the images that contain the company's logo, which are rare but important to detect. By weighing recall more than precision, we can emphasize the importance of finding all the positive examples, even if some false positives are included2.
* Option B is incorrect because using RMSE (root mean squared error) is not a valid metric for binary classification with imbalanced data. RMSE is a metric that measures the average magnitude of the errors between the predicted and actual values3. RMSE is suitable for regression problems, where the target variable is continuous, not for classification problems, where the target variable is discrete4.
* Option C is incorrect because using F1 score is not the best metric for binary classification with imbalanced data. F1 score is a special case of F-score where precision and recall are equally weighted1. F1 score is suitable for balanced data, where the positive and negative classes are equally important and frequent5. However, for imbalanced data, the positive class is more important and less frequent than the negative class, so F1 score may not reflect the performance of the model well2.
* Option D is incorrect because using F-score where precision is weighed more than recall is not a good metric for binary classification with imbalanced data. By weighing precision more than recall, we can
* emphasize the importance of minimizing the false positives, even if some true positives are missed2. However, for imbalanced data, the true positives are more important and less frequent than the false positives, so this metric may not reflect the performance of the model well2.
References:
* Precision, recall, and F-measure
* F-score for imbalanced data
* RMSE
* Regression vs classification
* F1 score
* [Imbalanced classification]
* [Binary classification]

NEW QUESTION # 154
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