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Google Professional Machine Learning Engineer certification is highly valued in the industry and can lead to excellent career opportunities for individuals with expertise in this field. Google Professional Machine Learning Engineer certification is a testament to a candidate's ability to design, develop, and deploy machine learning models, and it can be a valuable asset for anyone seeking a career in machine learning or data science. Additionally, the certification demonstrates a candidate's knowledge of Google Cloud technologies and their ability to use them effectively to solve real-world problems.
NEW QUESTION # 17
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. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.
- C. 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.
- D. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
Answer: A
NEW QUESTION # 18
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?
- A. Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate transformations Choose an automatic data split across the training, validation, and testing sets
- B. Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
- C. Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set
- D. Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately Choose an automatic data split across the training, validation, and testing sets
Answer: C
NEW QUESTION # 19
You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries:
CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.8);
CREATE OR REPLACE TABLE 'myproject.mydataset.validation' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.2);
After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?
- A. The tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table.
- B. There is not a sufficient amount of training data.
- C. The RAND() function generated a number that is less than 0.2 in both instances, so every record in the validation table will also be in the training table.
- D. There is training-serving skew in your production environment.
Answer: D
NEW QUESTION # 20
You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?
- A. Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table
- B. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery
- C. Convert your PySpark into SparkSQL queries to transform the data and then run your pipeline on Dataproc to write the data into BigQuery.
- D. Ingest your data into Cloud SQL convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning
Answer: C
NEW QUESTION # 21
You are working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?
- A. Address the model overfitting by using a less complex algorithm.
- B. Address data leakage by applying nested cross-validation during model training.
- C. Address data leakage by removing features highly correlated with the target value.
- D. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.
Answer: B
Explanation:
https://towardsdatascience.com/time-series-nested-cross-validation-76adba623eb9
NEW QUESTION # 22
A Machine Learning Specialist is working with a large cybersecurity company that manages security events in real time for companies around the world. The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested. The company also wants be able to save the results in its data lake for later processing and analysis.
What is the MOST efficient way to accomplish these tasks?
- A. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection. Then use Kinesis Data Firehose to stream the results to Amazon S3.
- B. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data. Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data.
- C. Ingest the data and store it in Amazon S3. Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.
- D. Ingest the data into Apache Spark Streaming using Amazon EMR, and use Spark MLlib with k-means to perform anomaly detection. Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake.
Answer: D
NEW QUESTION # 23
You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?
- A. Use the Al Platform Training built-in algorithms to create a custom model
- B. Use the Cloud Natural Language API to extract custom entities for classification
- C. Use AutoML Natural Language to extract custom entities for classification
- D. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm
Answer: A
NEW QUESTION # 24
You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?
- A. Develop a custom TensorFlow regression model, and optimize it using Vertex Al Training.
- B. Develop a custom scikit-learn regression model, and optimize it using Vertex Al Training
- C. Develop a custom PyTorch regression model, and optimize it using Vertex Al Training
- D. Develop a regression model using BigQuery ML.
Answer: D
Explanation:
BigQuery ML is a powerful tool that allows you to build and deploy machine learning models directly within BigQuery, Google's fully-managed, serverless data warehouse. It allows you to create regression models using SQL, which is a familiar and easy-to-use language for many data scientists. It also allows you to scale smoothly and require minimal development work since you don't have to worry about cluster management and it's fully-managed by Google.
BigQuery ML also allows you to run your training on the same data where it's stored, this will minimize data movement, and thus minimize cost and time.
Reference:
BigQuery ML
BigQuery ML for regression
BigQuery ML for scalability
NEW QUESTION # 25
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 training is reproducible
- B. Ensure that all hyperparameters are tuned
- C. Ensure that feature expectations are captured in the schema
- D. Ensure that model performance is monitored
Answer: A
Explanation:
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf
NEW QUESTION # 26
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. Search for duplicate accounts in the AWS Glue Data Catalog.
- C. Create an AWS Glue crawler to infer duplicate accounts in the source data.
- 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 # 27
You work for a gaming company that has millions of customers around the world. All games offer a chat feature that allows players to communicate with each other in real time. Messages can be typed in more than 20 languages and are translated in real time using the Cloud Translation API. You have been asked to build an ML system to moderate the chat in real time while assuring that the performance is uniform across the various languages and without changing the serving infrastructure.
You trained your first model using an in-house word2vec model for embedding the chat messages translated by the Cloud Translation API. However, the model has significant differences in performance across the different languages. How should you improve it?
- A. Add a regularization term such as the Min-Diff algorithm to the loss function.
- B. Remove moderation for languages for which the false positive rate is too high.
- C. Replace the in-house word2vec with GPT-3 or T5.
- D. Train a classifier using the chat messages in their original language.
Answer: B
NEW QUESTION # 28
You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company's weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter's published date and the user remains on the page for at least one minute.
All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model's performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?
- A. Schedule a weekly query in BigQuery to compute the success metric.
- B. Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.
- C. Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.
- D. Schedule a daily Dataflow job in Cloud Composer to compute the success metric.
Answer: D
NEW QUESTION # 29
A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively.
How should the Specialist address this issue and what is the reason behind it?
- A. The learning rate should be increased because the optimization process was trapped at a local minimum.
- B. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
- C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
- D. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
Answer: D
Explanation:
Explanation/Reference: https://www.tensorflow.org/tutorials/keras/overfit_and_underfit
NEW QUESTION # 30
A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet.
Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)
- A. Add a SageMaker Runtime VPC endpoint interface to the VPC.
- B. Add a VPC endpoint policy to allow access to the IAM users.
- C. Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.
- D. Modify the ACL on the endpoint network interface to restrict access to the instances.
- E. Modify the security group on the endpoint network interface to restrict access to the instances.
Answer: B,E
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/private-package-installation-in-amazon- sagemaker-running-in-internet-free-mode/
NEW QUESTION # 31
You are an ML engineer at a manufacturing company You are creating a classification model for a predictive maintenance use case You need to predict whether a crucial machine will fail in the next three days so that the repair crew has enough time to fix the machine before it breaks. Regular maintenance of the machine is relatively inexpensive, but a failure would be very costly You have trained several binary classifiers to predict whether the machine will fail. where a prediction of 1 means that the ML model predicts a failure.
You are now evaluating each model on an evaluation dataset. You want to choose a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure. Which model should you choose?
- A. The model with the highest recall where precision is greater than 0.5.
- B. The model with the highest precision where recall is greater than 0.5.
- C. The model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0 5
- D. The model with the lowest root mean squared error (RMSE) and recall greater than 0.5.
Answer: A
Explanation:
In predictive maintenance, the goal is to identify which machines are likely to fail soon, so that the repair crew can fix them before they break. In this context, it is important to prioritize detection, while also ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure.
Recall is a metric that measures the proportion of actual positive observations that are correctly predicted as such by the model. In this case, recall is a good metric to use because it measures how well the model is able to identify the machines that are likely to fail soon.
Precision is a metric that measures the proportion of positive predictions that are actually true. In this case, precision is also important because it measures how many of the machines that the model predicts will fail soon, actually do fail soon.
By combining these two metrics, you can ensure that your model is able to identify the machines that are likely to fail soon with a high degree of accuracy. In this case, the model with the highest recall where precision is greater than 0.5 will be the best model, as it will have a high ability to identify the machines that are likely to fail soon and also it will have a high degree of accuracy.
Reference:
Recall and Precision
Predictive Maintenance
Metrics for classification
NEW QUESTION # 32
A large company has developed a BI application that generates reports and dashboards using data collected from various operational metrics. The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports. The company wants the executives to be able ask questions using written and spoken interfaces.
Which combination of services can be used to build this conversational interface? (Choose three.)
- A. Amazon Lex
- B. Amazon Comprehend
- C. Amazon Transcribe
- D. Amazon Polly
- E. Alexa for Business
- F. Amazon Connect
Answer: B,C,F
NEW QUESTION # 33
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?
- A. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
- B. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
- C. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
- D. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
Answer: D
NEW QUESTION # 34
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