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Professional-Machine-Learning-Engineer Dumps Full Questions - Exam Study Guide
To prepare for the Google Professional Machine Learning Engineer Certification Exam, candidates must have a strong foundation in machine learning principles, algorithms, and data science. They must also have experience working with Google Cloud Platform and its tools for machine learning, such as Cloud ML Engine, BigQuery, and TensorFlow. Candidates can prepare for the exam by taking courses and training programs offered by Google Cloud or by studying the exam syllabus and practicing with sample questions.
To earn this certification, candidates must pass a rigorous exam that covers a wide range of topics related to machine learning and cloud computing. Professional-Machine-Learning-Engineer exam consists of multiple-choice and scenario-based questions, and candidates are given two and a half hours to complete the exam. Professional-Machine-Learning-Engineer exam is administered online and can be taken from anywhere in the world. Upon passing the exam, candidates will receive a digital badge that they can display on their LinkedIn profile, resume, or website, indicating that they have demonstrated proficiency in the field of machine learning and the Google Cloud Platform. Google Professional Machine Learning Engineer certification is recognized by industry professionals and can help individuals advance their careers in the field of machine learning and cloud computing.
NEW QUESTION # 12
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. Replace the age field value for records with a value of 0 with the mean or median value from the dataset
- B. Drop all records from the dataset where age has been set to 0.
- 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: B
Explanation:
Explanation
NEW QUESTION # 13
You work as an ML engineer at a social media company, and you are developing a visual filter for users' profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company's iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones. What should you do?
- A. Train a model using AutoML Vision and use the "export for Coral" option.
- B. Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).
- C. Train a model using AutoML Vision and use the "export for Core ML" option.
- D. Train a model using AutoML Vision and use the "export for TensorFlow.js" option.
Answer: C
NEW QUESTION # 14
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?
- A. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
- B. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
- C. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
- D. Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.
Answer: A
NEW QUESTION # 15
Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?
- A. 1. Create a Pub/Sub topic for each user
2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold - B. 1. Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold - C. 1 Build a notification system on Firebase
- D. 1. Create a Pub/Sub topic for each user
2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
Answer: C
Explanation:
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold Explanation:
Firebase is designed for exactly this sort of scenario. Also, it would not be possible to create millions of pubsub topics due to GCP quotas https://cloud.google.com/pubsub/quotas#quotas
https://firebase.google.com/docs/cloud-messaging
NEW QUESTION # 16
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen.
Which combination of algorithms would provide the appropriate insights? (Select TWO.)
- A. The Latent Dirichlet Allocation (LDA) algorithm
- B. The principal component analysis (PCA) algorithm
- C. The Random Cut Forest (RCF) algorithm
- D. The k-means algorithm
- E. The factorization machines (FM) algorithm
Answer: B,D
Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.
NEW QUESTION # 17
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. 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
- 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. 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.
- 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: A
Explanation:
https://cloud.google.com/ai-platform/prediction/docs/resource-labels#overview_of_labels You can add labels to your AI Platform Prediction jobs, models, and model versions, then use those labels to organize resources into categories when viewing or monitoring the resources. For example, you can label jobs by team (such as engineering or research) and development phase (prod or test), then filter the jobs based on the team and phase. Labels are also available on operations, but these labels are derived from the resource to which the operation applies. You cannot add or update labels on an operation.
https://cloud.google.com/ai-platform/prediction/docs/sharing-models.
NEW QUESTION # 18
You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?
- A. Compare the loss performance for each model on a held-out dataset.
- B. Compare the loss performance for each model on the validation data
- C. Compare the mean average precision across the models using the Continuous Evaluation feature
- D. Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool
Answer: B
NEW QUESTION # 19
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?
- A. Ensure that all hyperparameters are tuned
- B. Ensure that feature expectations are captured in the schema
- C. Ensure that model performance is monitored
- D. Ensure that training is reproducible
Answer: A
NEW QUESTION # 20
You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?
- A. Develop an image classification ML model to predict the presence of the disease.
- B. Develop an image segmentation ML model to locate the boundaries of the rust spots.
- C. Develop a template matching algorithm using traditional computer vision libraries.
- D. Create an object detection model that can localize the rust spots.
Answer: B
NEW QUESTION # 21
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": [['a', 'b\ 'c'1, [d\ 'e\ T]]})
- B. data = json.dumps({"signature_name": "serving_default'\ "instances": [fab', 'be1, 'cd']]})
- 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", 'd', 'e', 'f']]})
Answer: C
Explanation:
https://stackoverflow.com/questions/37956197/what-is-the-negative-index-in-shape-arrays-used-for-tensorflow
NEW QUESTION # 22
You have been given a dataset with sales predictions based on your company's marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the dat a. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks. You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?
- A. Train a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.
- B. Use BigQuery ML to run several regression models, and analyze their performance.
- C. Read the data from BigQuery using Dataproc, and run several models using SparkML.
- D. Use Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics.
Answer: B
NEW QUESTION # 23
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. Use Cloud Functions for prediction each time a new data point is ingested
- B. Use the batch prediction functionality of Al Platform
- C. Deploy the model on Al Platform and create a version of it for online inference.
- D. Create a serving pipeline in Compute Engine for prediction
Answer: B
Explanation:
https://cloud.google.com/ai-platform/prediction/docs/batch-predict
NEW QUESTION # 24
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 knowledge-based filtering model
- B. Build a collaborative-based filtering model
- C. Build a regression model using the features as predictors
- D. Build a classification model
Answer: B
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 # 25
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. Rewrite your input function using parallel reads, parallel processing, and prefetch.
- 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. Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.
Answer: D
NEW QUESTION # 26
You have successfully deployed to production a large and complex TensorFlow model trained on tabular dat a. You want to predict the lifetime value (LTV) field for each subscription stored in the BigQuery table named subscription. subscriptionPurchase in the project named my-fortune500-company-project.
You have organized all your training code, from preprocessing data from the BigQuery table up to deploying the validated model to the Vertex AI endpoint, into a TensorFlow Extended (TFX) pipeline. You want to prevent prediction drift, i.e., a situation when a feature data distribution in production changes significantly over time. What should you do?
- A. Add a model monitoring job where 10% of incoming predictions are sampled 24 hours.
- B. Add a model monitoring job where 90% of incoming predictions are sampled 24 hours.
- C. Add a model monitoring job where 10% of incoming predictions are sampled every hour.
- D. Implement continuous retraining of the model daily using Vertex AI Pipelines.
Answer: B
NEW QUESTION # 27
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 Cloud Bigtable, and read the data from Bigtable
- B. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)
- C. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
- D. Load the data into BigQuery and read the data from BigQuery.
Answer: A
NEW QUESTION # 28
You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?
- A. Downsample the data with upweighting to create a sample with 10% positive examples
- B. Remove negative examples until the numbers of positive and negative examples are equal
- C. Use the class distribution to generate 10% positive examples
- D. Use a convolutional neural network with max pooling and softmax activation
Answer: A
Explanation:
https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data#downsampling-and-upweighting
https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data
NEW QUESTION # 29
You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow. Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time. What should you do?
- A. Use a TPU with tf.distribute.TPUStrategy.
- B. Create a custom training loop.
- C. Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset
- D. Increase the batch size.
Answer: B
Explanation:
This would allow you to tailor the training process to your specific needs and requirements, and it would also allow for more flexible experimentation with different training strategies.
Additionally, creating a custom training loop could result in faster training times compared to using a single GPU or the distributed training strategies currently available in Keras.
NEW QUESTION # 30
You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?
- A. Import your user events and then your product catalog to make sure you have the highest quality event stream
- B. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.
- C. Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
- D. Use the "Other Products You May Like" recommendation type to increase the click-through rate
Answer: C
Explanation:
Frequently bought together' recommendations aim to up-sell and cross-sell customers by providing product.
NEW QUESTION # 31
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