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

Section	Objectives
ML model development	- Model training and tuning 1. Train models using TensorFlow / Vertex AI 2. Hyperparameter tuning and optimization - Evaluation 1. Model validation strategies 2. Evaluate model performance metrics
Data preparation and processing	- Feature engineering 1. Transform and preprocess datasets 2. Feature selection and representation techniques - Data ingestion and pipelines 1. Build data pipelines for training and serving 2. Use BigQuery and data processing services
ML pipeline automation and orchestration	- Pipeline design 1. Build end-to-end ML pipelines 2. Use Vertex AI Pipelines
Deployment and operations	- Monitoring and maintenance 1. Retraining and lifecycle management 2. Monitor model drift and performance - Model deployment 1. Batch and online prediction systems 2. Deploy models using Vertex AI endpoints
Designing ML solutions	- ML architecture design 1. Design scalable ML systems on GCP 2. Select appropriate ML models and approaches - 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 (Q54-Q59):

NEW QUESTION # 54
You work for the AI team of an automobile company, and you are developing a visual defect detection model using TensorFlow and Keras. To improve your model performance, you want to incorporate some image augmentation functions such as translation, cropping, and contrast tweaking. You randomly apply these functions to each training batch. You want to optimize your data processing pipeline for run time and compute resources utilization. What should you do?

A. Use Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords.
B. Use Dataflow to create all possible augmentations, and store them as TFRecords.
C. Embed the augmentation functions dynamically as part of Keras generators.
D. Embed the augmentation functions dynamically in the tf.Data pipeline.

Answer: D

Explanation:
The best option for optimizing the data processing pipeline for run time and compute resources utilization is to embed the augmentation functions dynamically in the tf.Data pipeline. This option has the following advantages:
* It allows the data augmentation to be performed on the fly, without creating or storing additional copies of the data. This saves storage space and reduces the data transfer time.
* It leverages the parallelism and performance of the tf.Data API, which can efficiently apply the augmentation functions to multiple batches of data in parallel, using multiple CPU cores or GPU devices. The tf.Data API also supports various optimization techniques, such as caching, prefetching, and autotuning, to improve the data processing speed and reduce the latency.
* It integrates seamlessly with the TensorFlow and Keras models, which can consume the tf.Data datasets as inputs for training and evaluation. The tf.Data API also supports various data formats, such as images, text, audio, and video, and various data sources, such as files, databases, and web services.
The other options are less optimal for the following reasons:
* Option B: Embedding the augmentation functions dynamically as part of Keras generators introduces some limitations and overhead. Keras generators are Python generators that yield batches of data for training or evaluation. However, Keras generators are not compatible with the tf.distribute API, which is used to distribute the training across multiple devices or machines. Moreover, Keras generators are not
* as efficient or scalable as the tf.Data API, as they run on a single Python thread and do not support parallelism or optimization techniques.
* Option C: Using Dataflow to create all possible augmentations, and store them as TFRecords introduces additional complexity and cost. Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. However, using Dataflow to create all possible augmentations requires generating and storing a large number of augmented images, which can consume a lot of storage space and incur storage and network costs. Moreover, using Dataflow to create the augmentations requires writing and deploying a separate Dataflow pipeline, which can be tedious and time-consuming.
* Option D: Using Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords introduces additional complexity and latency. Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. However, using Dataflow to create the augmentations dynamically per training run requires running a Dataflow pipeline every time the model is trained, which can introduce latency and delay the training process. Moreover, using Dataflow to create the augmentations requires writing and deploying a separate Dataflow pipeline, which can be tedious and time-consuming.
References:
* [tf.data: Build TensorFlow input pipelines]
* [Image augmentation | TensorFlow Core]
* [Dataflow documentation]

NEW QUESTION # 55
You work for a company that is developing an application to help users with meal planning You want to use machine learning to scan a corpus of recipes and extract each ingredient (e g carrot, rice pasta) and each kitchen cookware (e.g. bowl, pot spoon) mentioned Each recipe is saved in an unstructured text file What should you do?

A. Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe Evaluate the model's performance on a prelabeled dataset.
B. Create a multi-label text classification dataset on Vertex Al Create a test dataset and label each recipe that corresponds to its ingredients and cookware Train a multi-class classification model Evaluate the model's performance on a holdout dataset.
C. Create a text dataset on Vertex Al for entity extraction Create two entities called ingredient" and cookware" and label at least 200 examples of each entity Train an AutoML entity extraction model to extract occurrences of these entity types Evaluate performance on a holdout dataset.
D. Create a text dataset on Vertex Al for entity extraction Create as many entities as there are different ingredients and cookware Train an AutoML entity extraction model to extract those entities Evaluate the models performance on a holdout dataset.

Answer: C

Explanation:
Entity extraction is a natural language processing (NLP) task that involves identifying and extracting specific types of information from text, such as names, dates, locations, etc. Entity extraction can help you analyze a corpus of recipes and extract each ingredient and cookware mentioned in them. Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides a service for AutoML entity extraction, which allows you to create and train custom entity extraction models without writing any code. You can use Vertex AI to create a text dataset for entity extraction, and label your data with two entities: "ingredient" and "cookware". You need to label at least 200 examples of each entity type to train an AutoML entity extraction model. You can also use a holdout dataset to evaluate the performance of your model, such as precision, recall, and F1-score. This solution can help you build a machine learning model to scan a corpus of recipes and extract each ingredient and cookware mentioned in them, and use the results to help users with meal planning. Reference:
AutoML Entity Extraction | Vertex AI
Preparing data for AutoML Entity Extraction | Vertex AI

NEW QUESTION # 56
A Data Scientist wants to gain real-time insights into a data stream of GZIP files.
Which solution would allow the use of SQL to query the stream with the LEAST latency?

A. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
B. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
C. AWS Glue with a custom ETL script to transform the data.
D. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

Answer: B

Explanation:
Explanation/Reference: https://aws.amazon.com/big-data/real-time-analytics-featured-partners/

NEW QUESTION # 57
You work at a bank You have a custom tabular ML model that was provided by the bank's vendor. The training data is not available due to its sensitivity. The model is packaged as a Vertex Al Model serving container which accepts a string as input for each prediction instance. In each string the feature values are separated by commas. You want to deploy this model to production for online predictions, and monitor the feature distribution over time with minimal effort What should you do?

A. 1 Refactor the serving container to accept key-value pairs as input format.
2. Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint.
3. Create a Vertex Al Model Monitoring job with feature drift detection as the monitoring objective.
B. 1 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Ai endpoint.
2. Create a Vertex Al Model Monitoring job with feature drift detection as the monitoring objective, and provide an instance schema.
C. 1 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint.
2 Create a Vertex Al Model Monitoring job with feature skew detection as the monitoring objective and provide an instance schema.
D. 1 Refactor the serving container to accept key-value pairs as input format.
2 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint.
3. Create a Vertex Al Model Monitoring job with feature skew detection as the monitoring objective.

Answer: A

NEW QUESTION # 58
You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your models features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

A. Reinforcement Learning
B. Convolutional Neural Networks (CNN)
C. Recurrent Neural Networks (RNN)
D. Classification

Answer: A

NEW QUESTION # 59
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