PROFESSIONAL-MACHINE-LEARNING-ENGINEER VALID MOCK TEST - PROFESSIONAL-MACHINE-LEARNING-ENGINEER NEW STUDY QUESTIONS

Professional-Machine-Learning-Engineer Valid Mock Test - Professional-Machine-Learning-Engineer New Study Questions

Professional-Machine-Learning-Engineer Valid Mock Test - Professional-Machine-Learning-Engineer New Study Questions

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The Google Professional Machine Learning Engineer certification exam covers various topics related to machine learning, such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and deployment. Professionals who pass the exam demonstrate their ability to design and develop machine learning models that meet specific business requirements. Google Professional Machine Learning Engineer certification exam also covers various machine learning techniques such as deep learning, supervised and unsupervised learning, and reinforcement learning.

Google Professional Machine Learning Engineer exam is an advanced-level certification and requires a deep understanding of machine learning concepts and practices. To be eligible for this certification, individuals must have experience with machine learning frameworks, such as TensorFlow and Scikit-learn, and have the ability to use these frameworks to create machine learning models. Additionally, individuals must have experience with data preprocessing and data analysis, as well as experience with cloud computing, specifically on the Google Cloud Platform.

To be eligible for the exam, candidates should have experience in machine learning, including designing and implementing machine learning models, as well as experience with cloud-based machine learning services. Candidates should also have experience with data engineering, data analysis, and software engineering. Professional-Machine-Learning-Engineer Exam is intended for individuals who have at least three years of experience in the field, and who are able to demonstrate their knowledge through a combination of multiple choice and practical exam questions.

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Professional-Machine-Learning-Engineer New Study Questions, Exam Professional-Machine-Learning-Engineer Learning

Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) Practice exams (desktop and web-based) are designed solely to help you get your Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) certification on your first try. Our Google Professional-Machine-Learning-Engineer mock test will help you understand the Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) exam inside out and you will get better marks overall. It is only because you have practical experience of the Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) exam even before the exam itself.

Google Professional Machine Learning Engineer Sample Questions (Q78-Q83):

NEW QUESTION # 78
You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?

  • A. Split the training and test data based on time rather than a random split to avoid leakage
  • B. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
  • C. Normalize the data for the training, and test datasets as two separate steps.
  • D. Add more data to your test set to ensure that you have a fair distribution and sample for testing

Answer: D


NEW QUESTION # 79
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano. Scikit-team, and custom libraries. What should you do?

  • A. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
  • B. Create a library of VM images on Compute Engine; and publish these images on a centralized repository
  • C. Use the Al Platform custom containers feature to receive training jobs using any framework
  • D. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob

Answer: A


NEW QUESTION # 80
Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendation model that will suggest articles to readers that are similar to the articles they are currently reading. Which approach should you use?

  • A. Create a collaborative filtering system that recommends articles to a user based on the user's past behavior.
  • B. Manually label a few hundred articles, and then train an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories.
  • C. Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity.
  • D. Build a logistic regression model for each user that predicts whether an article should be recommended to a user.

Answer: C

Explanation:
Option A is incorrect because creating a collaborative filtering system that recommends articles to a user based on the user's past behavior is not the best approach to suggest articles that are similar to the articles they are currently reading. Collaborative filtering is a method of recommendation that uses the ratings or preferences of other users to predict the preferences of a target user1. However, this method does not consider the content or features of the articles, and may not be able to find articles that are similar in terms of topic, style, or sentiment.
Option B is correct because encoding all articles into vectors using word2vec, and building a model that returns articles based on vector similarity is a suitable approach to suggest articles that are similar to the articles they are currently reading. Word2vec is a technique that learns low-dimensional and dense representations of words from a large corpus of text, such that words that are semantically similar have similar vectors2. By applying word2vec to the articles, we can obtain vector representations of the articles that capture their meaning and usage. Then, we can use a similarity measure, such as cosine similarity, to find articles that have similar vectors to the current article3.
Option C is incorrect because building a logistic regression model for each user that predicts whether an article should be recommended to a user is not a feasible approach to suggest articles that are similar to the articles they are currently reading. Logistic regression is a supervised learning method that models the probability of a binary outcome (such as recommend or not) based on some input features (such as user profile or article content)4. However, this method requires a large amount of labeled data for each user, which may not be available or scalable. Moreover, this method does not directly measure the similarity between articles, but rather the likelihood of a user's preference.
Option D is incorrect because manually labeling a few hundred articles, and then training an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories is not an effective approach to suggest articles that are similar to the articles they are currently reading. SVM (support vector machine) is a supervised learning method that finds a hyperplane that separates the data into different classes (such as news categories) with the maximum margin5. However, this method also requires a large amount of labeled data, which may be costly and time-consuming to obtain. Moreover, this method does not account for the fine-grained similarity between articles within the same category, or the cross-category similarity between articles from different categories.
Reference:
Collaborative filtering
Word2vec
Cosine similarity
Logistic regression
SVM


NEW QUESTION # 81
Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?

  • A. 1 Dataflow, 2 BigQuery
  • B. 1 Dataflow, 2 Cloud SQL
  • C. 1 Cloud Function, 2 Cloud SQL
  • D. 1 Pub/Sub, 2 Datastore

Answer: A

Explanation:
A data pipeline is a set of steps or processes that move data from one or more sources to one or more destinations, usually for the purpose of analysis, transformation, or storage. A data pipeline can be designed using various components, such as data sources, data processing tools, data storage systems, and data analytics tools1 To design a data pipeline for analyzing customer sentiments in each call, one should consider the following requirements and constraints:
The call center receives over one million calls daily, and data is stored in Cloud Storage. This implies that the data is large, unstructured, and distributed, and requires a scalable and efficient data processing tool that can handle various types of data formats, such as audio, text, or image.
The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. This implies that the data is sensitive and subject to data privacy and compliance regulations, and requires a secure and reliable data storage system that can enforce data encryption, access control, and regional policies.
The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. This implies that the data analytics tool is external and independent of the data pipeline, and requires a standard and compatible data interface that can support SQL queries and operations.
One of the best options for selecting components for data processing and for analytics is to use Dataflow for data processing and BigQuery for analytics. Dataflow is a fully managed service for executing Apache Beam pipelines for data processing, such as batch or stream processing, extract-transform-load (ETL), or data integration. BigQuery is a serverless, scalable, and cost-effective data warehouse that allows you to run fast and complex queries on large-scale data23 Using Dataflow and BigQuery has several advantages for this use case:
Dataflow can process large and unstructured data from Cloud Storage in a parallel and distributed manner, and apply various transformations, such as converting audio to text, extracting sentiment scores, or anonymizing PII. Dataflow can also handle both batch and stream processing, which can enable real-time or near-real-time analysis of the call data.
BigQuery can store and analyze the processed data from Dataflow in a secure and reliable way, and enforce data encryption, access control, and regional policies. BigQuery can also support SQL ANSI-2011 compliant interface, which can enable the data science team to use their third-party tool for visualization and access. BigQuery can also integrate with various Google Cloud services and tools, such as AI Platform, Data Studio, or Looker.
Dataflow and BigQuery can work seamlessly together, as they are both part of the Google Cloud ecosystem, and support various data formats, such as CSV, JSON, Avro, or Parquet. Dataflow and BigQuery can also leverage the benefits of Google Cloud infrastructure, such as scalability, performance, and cost-effectiveness.
The other options are not as suitable or feasible. Using Pub/Sub for data processing and Datastore for analytics is not ideal, as Pub/Sub is mainly designed for event-driven and asynchronous messaging, not data processing, and Datastore is mainly designed for low-latency and high-throughput key-value operations, not analytics. Using Cloud Function for data processing and Cloud SQL for analytics is not optimal, as Cloud Function has limitations on the memory, CPU, and execution time, and does not support complex data processing, and Cloud SQL is a relational database service that may not scale well for large-scale data. Using Cloud Composer for data processing and Cloud SQL for analytics is not relevant, as Cloud Composer is mainly designed for orchestrating complex workflows across multiple systems, not data processing, and Cloud SQL is a relational database service that may not scale well for large-scale data.


NEW QUESTION # 82
You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performing well and you plan to deploy it to production. Due to compliance requirements the model must provide explanations for each prediction. You want to add this functionality to your model code with minimal effort and provide explanations that are as accurate as possible What should you do?

  • A. Update the custom serving container to include sampled Shapley-based explanations in the prediction outputs.
  • B. Upload the custom model to Vertex Al Model Registry and configure feature-based attribution by using sampled Shapley with input baselines.
  • C. Create an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable Al.
  • D. Create a BigQuery ML deep neural network model, and use the ML. EXPLAIN_PREDICT method with the num_integral_steps parameter.

Answer: B


NEW QUESTION # 83
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