AVAIL RELIABLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER TEST CRAM PDF TO PASS PROFESSIONAL-MACHINE-LEARNING-ENGINEER ON THE FIRST ATTEMPT

Avail Reliable Professional-Machine-Learning-Engineer Test Cram Pdf to Pass Professional-Machine-Learning-Engineer on the First Attempt

Avail Reliable Professional-Machine-Learning-Engineer Test Cram Pdf to Pass Professional-Machine-Learning-Engineer on the First Attempt

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Google Professional-Machine-Learning-Engineer Certification Exam is a professional-level certification exam that tests your proficiency in building and deploying machine learning models on Google Cloud Platform. It is designed for individuals with a solid understanding of machine learning concepts and experience in developing and deploying machine learning models on Google Cloud Platform. If you are a machine learning engineer, data scientist, or software developer looking to demonstrate your expertise in machine learning, this certification exam is an excellent way to showcase your skills and knowledge.

What is the duration, language, and format of Professional Machine Learning Engineer - Google

  • Language of Exam: English, Japanese, Korean
  • Type of Questions: Multiple choice (MCQs), multiple answers
  • Duration of Exam: 120 minutes
  • No negative marking for wrong answers

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Google Professional-Machine-Learning-Engineer Certification Exam is intended for machine learning engineers, data scientists, and software developers who want to demonstrate their expertise in building and deploying machine learning models on Google Cloud Platform. Professional-Machine-Learning-Engineer exam covers a wide range of topics, including data preparation and analysis, feature engineering, model selection and training, model evaluation and optimization, and deploying and managing machine learning models on Google Cloud Platform.

Google Professional Machine Learning Engineer Sample Questions (Q287-Q292):

NEW QUESTION # 287
As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

  • A. Create a serving pipeline in Compute Engine for prediction
  • B. Deploy the model on Al Platform and create a version of it for online inference.
  • C. Use the batch prediction functionality of Al Platform
  • D. Use Cloud Functions for prediction each time a new data point is ingested

Answer: C

Explanation:
https://cloud.google.com/ai-platform/prediction/docs/batch-predict


NEW QUESTION # 288
You are training a deep learning model for semantic image segmentation with reduced training time. While using a Deep Learning VM Image, you receive the following error: The resource 'projects/deeplearning-platforn/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80' was not found. What should you do?

  • A. Ensure that you have preemptible GPU quota in the selected region.
  • B. Ensure that the required GPU is available in the selected region.
  • C. Ensure that you have GPU quota in the selected region.
  • D. Ensure that the selected GPU has enough GPU memory for the workload.

Answer: C


NEW QUESTION # 289
You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

  • A. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.
  • B. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
  • C. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.
  • D. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.

Answer: D

Explanation:
Data Catalog is a fully managed and scalable metadata management service that allows you to quickly discover, manage, and understand your data in Google Cloud. You can use Data Catalog to search the BigQuery datasets by using keywords in the table description, as well as other metadata attributes such as table name, column name, labels, tags, and more. Data Catalog also provides a rich browsing experience that lets you explore the schema, preview the data, and access the BigQuery console directly from the Data Catalog UI. Data Catalog helps you find the data that you need for your model building on AI Platform without writing any code or queries.
References:
* [Data Catalog documentation]
* [Data Catalog overview]
* [Searching for data assets]


NEW QUESTION # 290
You are training an ML model using data stored in BigQuery that contains several values that are considered Personally Identifiable Information (Pll). You need to reduce the sensitivity of the dataset before training your model. Every column is critical to your model. How should you proceed?

  • A. Use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow with the DLP API to encrypt sensitive values with Format Preserving Encryption
  • B. Use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow to replace all sensitive data by using the encryption algorithm AES-256 with a salt.
  • C. Before training, use BigQuery to select only the columns that do not contain sensitive data Create an authorized view of the data so that sensitive values cannot be accessed by unauthorized individuals.
  • D. Using Dataflow, ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column.

Answer: A

Explanation:
The best option for reducing the sensitivity of the dataset before training the model is to use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow with the DLP API to encrypt sensitive values with Format Preserving Encryption. This option allows you to keep every column in the dataset, while protecting the sensitive data from unauthorized access or exposure. The Cloud DLP API can detect and classify various types of sensitive data, such as names, email addresses, phone numbers, credit card numbers, and more1. Dataflow can create scalable and reliable pipelines to process large volumes of data from BigQuery and other sources2. Format Preserving Encryption (FPE) is a technique that encrypts sensitive data while preserving its original format and length, which can help maintain the utility and validity of the data3.
By using Dataflow with the DLP API, you can apply FPE to the sensitive values in the dataset, and store the encrypted data in BigQuery or another destination. You can also use the same pipeline to decrypt the data when needed, by using the same encryption key and method4.
The other options are not as suitable as option B, for the following reasons:
* Option A: Using Dataflow to ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column, would reduce the sensitivity of the data, but also the utility and accuracy of the data. Randomization is a technique that replaces sensitive data with random values, which can prevent re-identification of the data, but also distort the distribution and relationships of the data3. This can affect the performance and quality of the ML model, especially if every column is critical to the model.
* Option C: Using the Cloud DLP API to scan for sensitive data, and use Dataflow to replace all sensitive data by using the encryption algorithm AES-256 with a salt, would reduce the sensitivity of the data, but also the utility and validity of the data. AES-256 is a symmetric encryption algorithm that uses a 256-bit key to encrypt and decrypt data. A salt is a random value that is added to the data before encryption, to increase the randomness and security of the encrypted data. However, AES-256 does not preserve the format or length of the original data, which can cause problems when storing or processing the data. For example, if the original data is a 10-digit phone number, AES-256 would produce a much longer and different string, which can break the schema or logic of the dataset3.
* Option D: Before training, using BigQuery to select only the columns that do not contain sensitive data, and creating an authorized view of the data so that sensitive values cannot be accessed by unauthorized individuals, would reduce the exposure of the sensitive data, but also the completeness and relevance of the data. An authorized view is a BigQuery view that allows you to share query results with particular users or groups, without giving them access to the underlying tables. However, this option assumes that you can identify the columns that do not contain sensitive data, which may not be easy or accurate.
Moreover, this option would remove some columns from the dataset, which can affect the performance and quality of the ML model, especially if every column is critical to the model.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 5: Responsible AI, Week
2: Privacy
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 5: Developing responsible AI solutions, 5.2 Implementing privacy techniques
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 9:
Responsible AI, Section 9.4: Privacy
* De-identification techniques
* Cloud Data Loss Prevention (DLP) API
* Dataflow
* Using Dataflow and Sensitive Data Protection to securely tokenize and import data from a relational database to BigQuery
* [AES encryption]
* [Salt (cryptography)]
* [Authorized views]


NEW QUESTION # 291
You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?

  • A. Build a custom TensorFlow neural network by using Vertex Al custom training Configure the model to generate explanations when it makes predictions.
  • B. Build an AutoML tabular regression model Configure the model to generate explanations when it makes predictions.
  • C. Build a random forest regression model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance's after the model is trained.
  • D. Build a random forest classification model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance's after the model is trained.

Answer: D

Explanation:
A random forest is an ensemble learning method that consists of many decision trees. It can be used for both regression and classification tasks. A random forest classification model can predict the probability of churn for each customer by assigning them to different classes, such as high-risk, medium-risk, or low-risk. A random forest model can also generate feature importances, which measure how much each feature contributes to the prediction. Feature importances can help interpret the model and understand what factors influence customer churn. Vertex AI Workbench is an integrated development environment (IDE) that allows you to create and run Jupyter notebooks on Google Cloud. You can use Vertex AI Workbench to build a random forest classification model in Python, using libraries such as scikit-learn or TensorFlow. You can also configure the model to generate feature importances after the model is trained, and visualize them using plots or tables. This solution can help you build an interpretable model for customer churn prediction, and use the results to design marketing campaigns that target at-risk customers. Reference:
Random Forests | scikit-learn
Vertex AI Workbench | Google Cloud
Interpreting Random Forests | Towards Data Science


NEW QUESTION # 292
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