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

Section	Objectives
Topic 1: ML model development	- Evaluation 1. Model validation strategies 2. Evaluate model performance metrics - Model training and tuning 1. Hyperparameter tuning and optimization 2. Train models using TensorFlow / Vertex AI
Topic 2: Deployment and operations	- Monitoring and maintenance 1. Monitor model drift and performance 2. Retraining and lifecycle management - Model deployment 1. Batch and online prediction systems 2. Deploy models using Vertex AI endpoints
Topic 3: ML pipeline automation and orchestration	- Pipeline design 1. Build end-to-end ML pipelines 2. Use Vertex AI Pipelines
Topic 4: 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
Topic 5: Data preparation and processing	- Data ingestion and pipelines 1. Build data pipelines for training and serving 2. Use BigQuery and data processing services - Feature engineering 1. Feature selection and representation techniques 2. Transform and preprocess datasets

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Google Professional-Machine-Learning-Engineer최신 덤프공부자료 & Professional-Machine-Learning-Engineer최신 덤프문제보기

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최신 Google Cloud Certified Professional-Machine-Learning-Engineer 무료샘플문제 (Q159-Q164):

질문 # 159
You work at a mobile gaming startup that creates online multiplayer games Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience. You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated Your model has performed well during testing, and you now need to deploy the model to production You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue. What should you do?

A. Import the model into Vertex Al Model Registry Create a Vertex Al endpoint that hosts the model and make online inference requests.
B. Import the model into Vertex Al Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.
C. Save the model files in a VM Load the model files each time there is a prediction request and run an inference job on the VM.
D. Save the model files in a Cloud Storage Bucket Create a Cloud Function to read the model files and make online inference requests on the Cloud Function.

정답：A

설명：
Online inference is a process where you send a single or a small number of prediction requests to a model and get immediate responses1. Online inference is suitable for scenarios where you need timely predictions, such as detecting cheating in online games. Online inference requires that the model is deployed to an endpoint, which is a resource that provides a service URL for prediction requests2.
Vertex AI Model Registry is a central repository where you can manage the lifecycle of your ML models3. You can import models from various sources, such as custom models or AutoML models, and assign them to different versions and aliases3. You can also deploy models to endpoints, which are resources that provide a service URL for online prediction2.
By importing the model into Vertex AI Model Registry, you can leverage the Vertex AI features to monitor and update the model3. You can use Vertex AI Experiments to track and compare the metrics of different model versions, such as accuracy, precision, recall, and AUC. You can also use Vertex AI Explainable AI to generate feature attributions that show how much each input feature contributed to the model's prediction.
By creating a Vertex AI endpoint that hosts the model, you can use the Vertex AI Prediction service to serve online inference requests2. Vertex AI Prediction provides various benefits, such as scalability, reliability, security, and logging2. You can use the Vertex AI API or the Google Cloud console to send online inference requests to the endpoint and get immediate classifications4.
Therefore, the best option for your scenario is to import the model into Vertex AI Model Registry, create a Vertex AI endpoint that hosts the model, and make online inference requests.
The other options are not suitable for your scenario, because they either do not provide immediate classifications, such as using batch prediction or loading the model files each time, or they do not use Vertex AI Prediction, which would require more development and maintenance effort, such as creating a Cloud Function or a VM.
Reference:
Online versus batch prediction | Vertex AI | Google Cloud
Deploy a model to an endpoint | Vertex AI | Google Cloud
Introduction to Vertex AI Model Registry | Google Cloud
Get online predictions | Vertex AI | Google Cloud

질문 # 160
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 20 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables.
This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

A. Submit the data for training without performing any manual transformations.
AIlow AutoML to handle the appropriate transformations.
Choose an automatic data split across the training, validation, and testing sets.
B. Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column.
AIlow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets.
C. Submit the data for training without performing any manual transformations.
Use the columns that have a time signal to manually split your data.
Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing sets from 30 days after your validation set.
D. Manually combine all columns that contain a time signal into an array.
AIlow AutoML to interpret this array appropriately.
Choose an automatic data split across the training, validation, and testing sets.

정답：C

설명：
https://cloud.google.com/automl-tables/docs/data-best-practices#time
The time signal that is spread across multiple columns so manual split is required.
- If the time information is not contained in a single column, you can use a manual data split to use the most recent data as the test data, and the earliest data as the training data.

질문 # 161
Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

A. Use an established text classification model on Al Platform as-is to classify support requests
B. Use an established text classification model on Al Platform to perform transfer learning
C. Use AutoML Natural Language to build the support requests classifier
D. Use the Natural Language API to classify support requests

정답：B

설명：
Transfer learning is a technique that leverages the knowledge and weights of a pre-trained model and adapts them to a new task or domain1. Transfer learning can save time and resources by avoiding training a model from scratch, and can also improve the performance and generalization of the model by using a larger and more diverse dataset2. AI Platform provides several established text classification models that can be used for transfer learning, such as BERT, ALBERT, or XLNet3. These models are based on state-of-the-art natural language processing techniques and can handle various text classification tasks, such as sentiment analysis, topic classification, or spam detection4. By using one of these models on AI Platform, you can customize the model's code, serving, and deployment, and use Kubeflow pipelines for the ML platform. Therefore, using an established text classification model on AI Platform to perform transfer learning is the best option for this use case.
References:
* Transfer Learning - Machine Learning's Next Frontier
* A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning
* Text classification models
* Text Classification with Pre-trained Models in TensorFlow

질문 # 162
You are profiling the performance of your TensorFlow model training time and notice a performance issue caused by inefficiencies in the input data pipeline for a single 5 terabyte CSV file dataset on Cloud Storage.
You need to optimize the input pipeline performance. Which action should you try first to increase the efficiency of your pipeline?

A. Set the reshuffle_each_iteration parameter to true in the tf.data.Dataset.shuffle method.
B. Preprocess the input CSV file into a TFRecord file.
C. Randomly select a 10 gigabyte subset of the data to train your model.
D. Split into multiple CSV files and use a parallel interleave transformation.

정답：B

설명：
According to the web search results, the TFRecord format is a recommended way to store large amounts of data efficiently and improve the performance of the data input pipeline123. The TFRecord format is a binary format that can be compressed and serialized, which reduces the I/O overhead and the memory footprint of the data1. The tf.data API provides tools to create and read TFRecord files easily1.
The other options are not as effective as option A. Option B would reduce the amount of data available for training and might affect the model accuracy. Option C would still require reading from a single CSV file at a time, which might not utilize the full bandwidth of the remote storage. Option D would only affect the order of the data elements, not the speed of reading them.

질문 # 163
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?

A. Load the data into BigQuery and read the data from BigQuery.
B. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
C. Load the data into Cloud Bigtable, and read the data from Bigtable
D. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)

정답：B

설명：
The input/output execution performance of a TensorFlow model depends on how efficiently the model can read and process the data from the data source. Reading and processing data from CSV files can be slow and inefficient, especially if the data is large and distributed. Therefore, to improve the input/output execution performance, one should use a more suitable data format and storage system.
One of the best options for improving the input/output execution performance is to convert the CSV files into shards of TFRecords, and store the data in Cloud Storage. TFRecord is a binary data format that can store a sequence of serialized TensorFlow examples. TFRecord has several advantages over CSV, such as:
* Faster data loading: TFRecord can be read and processed faster than CSV, as it avoids the overhead of parsing and decoding the text data. TFRecord also supports compression and checksums, which can reduce the data size and ensure data integrity1
* Better performance: TFRecord can improve the performance of the model, as it allows the model to access the data in a sequential and streaming manner, and leverage the tf.data API to build efficient data pipelines. TFRecord also supports sharding and interleaving, which can increase the parallelism and throughput of the data processing2
* Easier integration: TFRecord can integrate seamlessly with TensorFlow, as it is the native data format for TensorFlow. TFRecord also supports various types of data, such as images, text, audio, and video, and can store the data schema and metadata along with the data3 Cloud Storage is a scalable and reliable object storage service that can store any amount of data. Cloud Storage has several advantages over other storage systems, such as:
* High availability: Cloud Storage can provide high availability and durability for the data, as it replicates the data across multiple regions and zones, and supports versioning and lifecycle management. Cloud Storage also offers various storage classes, such as Standard, Nearline, Coldline, and Archive, to meet different performance and cost requirements4
* Low latency: Cloud Storage can provide low latency and high bandwidth for the data, as it supports HTTP and HTTPS protocols, and integrates with other Google Cloud services, such as AI Platform, Dataflow, and BigQuery. Cloud Storage also supports resumable uploads and downloads, and parallel composite uploads, which can improve the data transfer speed and reliability5
* Easy access: Cloud Storage can provide easy access and management for the data, as it supports various tools and libraries, such as gsutil, Cloud Console, and Cloud Storage Client Libraries. Cloud Storage
* also supports fine-grained access control and encryption, which can ensure the data security and privacy.
The other options are not as effective or feasible. Loading the data into BigQuery and reading the data from BigQuery is not recommended, as BigQuery is mainly designed for analytical queries on large-scale data, and does not support streaming or real-time data processing. Loading the data into Cloud Bigtable and reading the data from Bigtable is not ideal, as Cloud Bigtable is mainly designed for low-latency and high-throughput key-value operations on sparse and wide tables, and does not support complex data types or schemas.
Converting the CSV files into shards of TFRecords and storing the data in the Hadoop Distributed File System (HDFS) is not optimal, as HDFS is not natively supported by TensorFlow, and requires additional configuration and dependencies, such as Hadoop, Spark, or Beam.
References: 1: TFRecord and tf.Example 2: Better performance with the tf.data API 3: TensorFlow Data Validation 4: Cloud Storage overview 5: Performance : [How-to guides]

질문 # 164
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