VALID TEST PROFESSIONAL-MACHINE-LEARNING-ENGINEER FEE AND GOOGLE OFFICIAL PROFESSIONAL-MACHINE-LEARNING-ENGINEER STUDY GUIDE: GOOGLE PROFESSIONAL MACHINE LEARNING ENGINEER LATEST RELEASED

Valid Test Professional-Machine-Learning-Engineer Fee and Google Official Professional-Machine-Learning-Engineer Study Guide: Google Professional Machine Learning Engineer Latest Released

Valid Test Professional-Machine-Learning-Engineer Fee and Google Official Professional-Machine-Learning-Engineer Study Guide: Google Professional Machine Learning Engineer Latest Released

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Google Professional Machine Learning Engineer certification is a highly sought-after certification in the field of machine learning. Google Professional Machine Learning Engineer certification is designed for professionals who are looking to validate their expertise in designing, building and deploying machine learning models using the Google Cloud Platform. Google Professional Machine Learning Engineer certification exam tests the candidate's ability to apply machine learning technologies to real-world scenarios.

The Google Professional Machine Learning Engineer certification exam is divided into several sections, each of which focuses on a specific aspect of machine learning. The sections include data preparation, model building, model deployment, and monitoring. Each section is designed to test the individual's ability to apply machine learning concepts in a practical setting. Professional-Machine-Learning-Engineer Exam Format includes multiple-choice questions, case studies, and hands-on exercises, which measure the individual's ability to apply machine learning concepts to real-world scenarios.

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Google Professional Machine Learning Engineer Certification Exam is a comprehensive test designed to assess an individual's proficiency in implementing and deploying machine learning models using Google Cloud Platform. Google Professional Machine Learning Engineer certification is designed for professionals who have experience in machine learning and want to demonstrate their skills and expertise in the field. Google Professional Machine Learning Engineer certification exam requires candidates to demonstrate their knowledge of machine learning principles, algorithms, data preparation, and model implementation.

Google Professional Machine Learning Engineer Sample Questions (Q285-Q290):

NEW QUESTION # 285
You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100 000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training and are stored in a Cloud Storage bucket You need to be able to tune the model during each training run. How should you train the model?

  • A. Develop the model training code for image classification and train a model by using Vertex Al custom training.
  • B. Train a model for object detection by using Vertex Al AutoML.
  • C. Train a model for image classification by using Vertex Al AutoML.
  • D. Develop the model training code for object detection and tram a model by using Vertex Al custom training.

Answer: A

Explanation:
Image classification is a task where the model assigns a label to an image based on its content, such as "stop sign" or "speed limit"1. Object detection is a task where the model locates and identifies multiple objects in an image, and draws bounding boxes around them2. Since your dataset consists of images that were cropped to show one out of ten different traffic signs, you are dealing with an image classification problem, not an object detection problem. Therefore, you need to train a model for image classification, not object detection.
Vertex AI AutoML is a service that allows you to train and deploy high-quality ML models with minimal effort and machine learning expertise3. You can use Vertex AI AutoML to train a model for image classification by uploading your images and labels to a Vertex AI dataset, and then launching an AutoML training job4. However, Vertex AI AutoML does not allow you to tune the model during each training run, as it automatically selects the best model architecture and hyperparameters for your data4.
Vertex AI custom training is a service that allows you to train and deploy your own custom ML models using your own code and frameworks5. You can use Vertex AI custom training to train a model for image classification by writing your own model training code, such as using TensorFlow or PyTorch, and then creating and running a custom training job. Vertex AI custom training allows you to tune the model during each training run, as you can specify the model architecture and hyperparameters in your code, and use Vertex AI Hyperparameter Tuning to optimize them .
Therefore, the best option for your scenario is to develop the model training code for image classification and train a model by using Vertex AI custom training.
References:
* Image classification | TensorFlow Core
* Object detection | TensorFlow Core
* Introduction to Vertex AI AutoML | Google Cloud
* AutoML Vision | Google Cloud
* Introduction to Vertex AI custom training | Google Cloud
* [Custom training with TensorFlow | Vertex AI | Google Cloud]
* [Hyperparameter tuning overview | Vertex AI | Google Cloud]


NEW QUESTION # 286
You work for an international manufacturing organization that ships scientific products all over the world Instruction manuals for these products need to be translated to 15 different languages Your organization's leadership team wants to start using machine learning to reduce the cost of manual human translations and increase translation speed. You need to implement a scalable solution that maximizes accuracy and minimizes operational overhead. You also want to include a process to evaluate and fix incorrect translations. What should you do?

  • A. Use AutoML Translation to tram a model Configure a Translation Hub project and use the trained model to translate the documents Use human reviewers to evaluate the incorrect translations
  • B. Create a Vertex Al pipeline that processes the documents1 launches an AutoML Translation training job evaluates the translations, and deploys the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between training and live data re-trigger the pipeline with the latest data.
  • C. Use Vertex Al custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data Deploy the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.
  • D. Create a workflow using Cloud Function Triggers Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket Configure another Cloud Function that translates the documents using the Cloud Translation API and saves the translations to an output Cloud Storage bucket Use human reviewers to evaluate the incorrect translations.

Answer: C


NEW QUESTION # 287
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 the Hadoop Distributed File System (HDFS)
  • 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 Cloud Storage

Answer: D

Explanation:
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]


NEW QUESTION # 288
You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?

  • A. Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.
  • B. Rewrite your input function to resize and reshape the input images.
  • C. Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.
  • D. Rewrite your input function using parallel reads, parallel processing, and prefetch.

Answer: D

Explanation:
The trace in the question shows that the training time is taking longer than expected. This is likely due to the input function not being optimized. To decrease training time in a cost-efficient way, the best option is to rewrite the input function using parallel reads, parallel processing, and prefetch. This will allow the model to process the data more efficiently and decrease training time. References:
* [Cloud TPU Performance Guide]
* [Data input pipeline performance guide]


NEW QUESTION # 289
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 Google-managed encryption keys to encrypt the Pll data at rest, and decrypt the Pll data during data exploration and preprocessing.
  • B. Use customer-managed encryption keys (CMEK) to encrypt the Pll data at rest and decrypt the Pll 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 PI! before performing data exploration and preprocessing.

Answer: D

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "design, build, and productionalize ML models to solve business challenges using Google Cloud technologies". Cloud Data Loss Prevention (DLP) API2 is a service that provides programmatic access to a powerful detection engine for personally identifiable information and other privacy-sensitive data in unstructured data streams, such as text blocks and images. Cloud DLP API helps you discover, classify, and protect your sensitive data by using techniques such as de-identification, masking, tokenization, and bucketing. You can use Cloud DLP API to de-identify the PII data before performing data exploration and preprocessing, and retain the data utility for ML purposes. Therefore, option A is the best way to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields. The other options are not relevant or optimal for this scenario. References:
* Professional ML Engineer Exam Guide
* Cloud Data Loss Prevention (DLP) API
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions


NEW QUESTION # 290
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