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Google Cloud Certified Professional-Machine-Learning-Engineer Exam and Certification Test Engine
Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Pipeline Automation & Orchestration
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
Design pipeline. Considerations include:
- Decoupling components with Cloud Build
- Testing for target performance
- Constructing and testing of parameterized pipeline definition in SDK
- Storing data and generated artifacts
- Organization and tracking experiments and pipeline runs
- Implement training pipeline
- Hybrid or multi-cloud strategies
- Setup of trigger and pipeline schedule
- Model binary options
- Identification of components, parameters, triggers, and compute needs
- Google Cloud serving options
- Use CI/CD to test and deploy models
- Model/dataset lineage
- A/B and canary testing
- Orchestration framework
- Hooking into model and dataset versioning
- Performing data validation
- Hooking models into existing CI/CD deployment system
- Tuning compute performance
NEW QUESTION 43
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?
- A. Create a FindMatches machine learning transform in AWS Glue.
- B. Create an AWS Glue crawler to infer duplicate accounts in the source data.
- C. Search for duplicate accounts in the AWS Glue Data Catalog.
- D. Execute the built-in FindDuplicates Amazon Athena query.
Answer: A
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html
NEW QUESTION 44
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. Add more data to your test set to ensure that you have a fair distribution and sample for testing
- 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. Split the training and test data based on time rather than a random split to avoid leakage
- D. Normalize the data for the training, and test datasets as two separate steps.
Answer: A
NEW QUESTION 45
A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.
How should the Data Science team configure the notebook instance placement to meet these requirements?
- A. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
- B. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
- C. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.
- D. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker.
Answer: D
NEW QUESTION 46
Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:
A)
B)
C)
D)
- A. Option D
- B. Option A
- C. Option B
- D. Option C
Answer: C
Explanation:
If we just put inside the Training set , Validation set and Test set , randomly Text, Paragraph or sentences the model will have the ability to learn specific qualities about The Author's use of language beyond just his own articles. Therefore the model will mixed up different opinions. Rather if we divided things up a the author level, so that given authors were only on the training data, or only in the test data or only in the validation data. The model will find more difficult to get a high accuracy on the test validation (What is correct and have more sense!). Because it will need to really focus in author by author articles rather than get a single political affiliation based on a bunch of mixed articles from different authors. https://developers.google.com/machine-learning/crash-course/18th-century-literature For example, suppose you are training a model with purchase data from a number of stores. You know, however, that the model will be used primarily to make predictions for stores that are not in the training data. To ensure that the model can generalize to unseen stores, you should segregate your data sets by stores. In other words, your test set should include only stores different from the evaluation set, and the evaluation set should include only stores different from the training set. https://cloud.google.com/automl-tables/docs/prepare#ml-use
NEW QUESTION 47
You are building an ML model to detect anomalies in real-time sensor dat a. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?
- A. 1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage
- B. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable
- C. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions
- D. 1 = Dataflow, 2 - Al Platform, 3 = BigQuery
Answer: C
NEW QUESTION 48
Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input dat a. How should you address the input differences in production?
- A. Perform feature selection on the model, and retrain the model on a monthly basis with fewer features
- B. Create alerts to monitor for skew, and retrain the model.
- C. Perform feature selection on the model, and retrain the model with fewer features
- D. Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service
Answer: D
NEW QUESTION 49
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?
- A. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
- B. Create a NAT gateway within the corporate VPC.
- C. Route Amazon SageMaker traffic through an on-premises network.
- D. Create VPC peering with Amazon VPC hosting Amazon SageMaker.
Answer: B
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf (46)
NEW QUESTION 50
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
- A. Use Al Platform Notebooks to run the classification model with pandas library
- B. Run a BigQuery ML task to perform logistic regression for the classification
- C. Use Al Platform to run the classification model job configured for hyperparameter tuning
- D. Configure AutoML Tables to perform the classification task
Answer: A
NEW QUESTION 51
Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?
- A. The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is
13.12% - B. The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is
20.85%*0.32 - C. The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is
20.85% - D. The true class frequency for Romance is 0.78 and the predicted class frequency for Adventure is (0.47-
0.32)
Answer: A
NEW QUESTION 52
You are going to train a DNN regression model with Keras APIs using this code:
How many trainable weights does your model have? (The arithmetic below is correct.)
- A. 501*256+257*128+2 = 161154
- B. 500*256*0 25+256*128*0 25+128*2 = 40448
- C. 500*256+256*128+128*2 = 161024
- D. 501*256+257*128+128*2=161408
Answer: D
NEW QUESTION 53
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?
- A. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
- B. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters
- C. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
- D. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.
Answer: A
NEW QUESTION 54
You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?
- A. Build a classification model
- B. Build a knowledge-based filtering model
- C. Build a regression model using the features as predictors
- D. Build a collaborative-based filtering model
Answer: D
Explanation:
Reference:
https://developers.google.com/machine-learning/recommendation/collaborative/basics
https://cloud.google.com/architecture/recommendations-using-machine-learning-on-compute-engine#filtering_the_data
NEW QUESTION 55
You work on a growing team of more than 50 data scientists who all use Al Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?
- A. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about Al Platform resource usage In BigQuery create a SQL view that maps users to the resources they are using.
- B. Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.
- C. Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources
- D. Set up restrictive I AM permissions on the Al Platform notebooks so that only a single user or group can access a given instance.
Answer: B
NEW QUESTION 56
A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent.
How should the Specialist frame this business problem?
- A. Multi-category classification
- B. Regression classification
- C. Streaming classification
- D. Binary classification
Answer: A
NEW QUESTION 57
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
- A. Run a BigQuery ML task to perform logistic regression for the classification
- B. Use Al Platform Notebooks to run the classification model with pandas library
- C. Use Al Platform to run the classification model job configured for hyperparameter tuning
- D. Configure AutoML Tables to perform the classification task
Answer: A
Explanation:
BigQuery ML supports supervised learning with the logistic regression model type.
NEW QUESTION 58
An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?
- A. Listwise deletion
- B. Multiple imputation
- C. Last observation carried forward
- D. Mean substitution
Answer: B
Explanation:
Explanation/Reference: https://worldwidescience.org/topicpages/i/imputing+missing+values.html
NEW QUESTION 59
You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set. What should you do?
- A. Extend your test dataset with images of the newer products when they are introduced to retraining
- B. Keep the original test dataset unchanged even if newer products are incorporated into retraining
- C. Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.
- D. Replace your test dataset with images of the newer products when they are introduced to retraining.
Answer: A
NEW QUESTION 60
Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?
- A. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.
2. Dispatch an available shuttle and provide the map with the required stops based on the prediction - B. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction. - C. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome. - D. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.
2 Dispatch an appropriately sized shuttle and indicate the required stops on the map
Answer: D
Explanation:
This is a case where machine learning would be terrible, as it would not be 100% accurate and some passengers would not get picked up. A simple algorith works better here, and the question confirms customers will be indicating when they are at the stop so no ML required.
NEW QUESTION 61
A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.
Which approach should the Specialist use to continue working?
- A. Download the SageMaker notebook to their local environment, then install Jupyter Notebooks on their laptop and continue the development in a local notebook.
- B. Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.
- C. Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.
- D. Install Python 3 and boto3 on their laptop and continue the code development using that environment.
Answer: D
Explanation:
Explanation
NEW QUESTION 62
A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences, and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?
- A. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database.
- B. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database.
- C. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database.
- D. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
Answer: B
Explanation:
Explanation
NEW QUESTION 63
Your company manages a video sharing website where users can watch and upload videos. You need to create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company's website. Which result should you use to determine whether the model is successful?
- A. The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.
- B. The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded.
- C. The model predicts videos as popular if the user who uploads them has over 10,000 likes.
- D. The model predicts 97.5% of the most popular clickbait videos measured by number of clicks.
Answer: B
Explanation:
https://developers.google.com/machine-learning/problem-framing/framing#quantify-it
NEW QUESTION 64
A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population How should the Data Scientist correct this issue?
- A. Drop all records from the dataset where age has been set to 0.
- B. Replace the age field value for records with a value of 0 with the mean or median value from the dataset
- C. Use k-means clustering to handle missing features
- D. Drop the age feature from the dataset and train the model using the rest of the features.
Answer: A
Explanation:
Explanation
NEW QUESTION 65
You trained a text classification model. You have the following SignatureDefs:
What is the correct way to write the predict request?
- A. data = json.dumps({"signature_name": "serving_default'\ "instances": [fab', 'be1, 'cd']]})
- B. data = json dumps({"signature_name": "serving_default"! "instances": [['a', 'b', "c", 'd', 'e', 'f']]})
- C. data = json dumps({"signature_name": f,serving_default", "instances": [['a', 'b'], [c\ 'd'], ['e\ T]]})
- D. data = json.dumps({"signature_name": "serving_default, "instances": [['a', 'b\ 'c'1, [d\ 'e\ T]]})
Answer: B
NEW QUESTION 66
Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:
A)
B)
C)
D)
- A. Option D
- B. Option B
- C. Option A
- D. Option C
Answer: D
NEW QUESTION 67
......
Prerequisites
The Google Professional Machine Learning Engineer certification exam has no formal prerequisites. However, it is pretty hard to pass this test without having solid practical background. The candidates are recommended to have at least three years of industry experience, involving about one year of experience in designing and managing solutions with the help of Google Cloud. The target individuals can take advantage of Google Cloud Free Tier to use the selected products free of charge and gain the real-world expertise.
Exam Topics
The successful performance in the Google Professional Machine Learning Engineer certification test requires a good comprehension of its topics. The exam syllabus consists of six sections that are described below:
- Architecting Machine Learning Solutions
Here the examinees need to demonstrate their proficiency in designing reliable, scalable, and highly available Machine Learning solutions. Besides that, the test takers need to be capable of selecting the proper Google Cloud hardware components, including evaluating accelerator and compute options (for example, CPU, TPU, GPU, edge devices). Lastly, they need to have the expertise in designing an architecture that meets the security concerns across the industries/sectors.
- Monitoring, Optimizing, and Maintaining Machine Learning Solutions
This objective evaluates the competency of the applicants in monitoring and troubleshooting the Machine Learning solutions. The individuals should also be able to tune the performance of Machine Learning for training and serving in production. This involves the ability to optimize and simplify the input pipeline for training as well as knowledge of the simplification techniques.
- Automating & Orchestrating Machine Learning Pipelines
This module encompasses one’s competency in designing & implementing training pipelines. This includes your ability to define the components, triggers, parameters, and compute needs; understanding of the orchestration framework; familiarity with the multi-Cloud or hybrid strategies; knowledge of system design involving the TFX components/Kubeflow DSL. The candidates should also possess the skills in implementing serving pipelines, including serving (online, caching, batch), testing for target performance, configuring trigger & pipeline schedules, among other skills. Apart from that, this part requires the students’ expertise in tracking & auditing metadata.
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