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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:

• Organization and tracking experiments and pipeline runs
• Implement serving pipeline
• A/B and canary testing
• Tuning compute performance
• Implement training pipeline
• Hooking models into existing CI/CD deployment system
• Constructing and testing of parameterized pipeline definition in SDK
• Use CI/CD to test and deploy models
• Testing for target performance
• Orchestration framework
• Setup of trigger and pipeline schedule
• Model binary options
• Decoupling components with Cloud Build
• Performing data validation
• Hybrid or multi-cloud strategies
• Track and audit metadata
• Storing data and generated artifacts

Google Professional Machine Learning Engineer Sample Questions (Q101-Q106):

NEW QUESTION # 101
A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.
What should the company do to reduce the processing time of loan applications?

• A. Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I).
  Perform a manual review on those words before performing a business validation.
• B. Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth.
  Perform a manual review on those words before performing a business validation.
• C. Use an Amazon Textract synchronous operation instead of an asynchronous operation.
• D. Use Amazon Rekognition's feature to detect text in an image to extract the data from scanned images. Use this information to process the loan applications.

Answer: A
NEW QUESTION # 102
A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3.
The source systems send data in .CSV format in real time. The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3.
Which solution takes the LEAST effort to implement?

• A. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.
• B. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
• C. Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet
• D. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.

Answer: B Explanation:
Explanation/Reference:
NEW QUESTION # 103
The displayed graph is from a forecasting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

• A. The model predicts both the trend and the seasonality well
• B. The model predicts the seasonality well, but not the trend.
• C. The model does not predict the trend or the seasonality well.
• D. The model predicts the trend well, but not the seasonality.

Answer: C
NEW QUESTION # 104
You started 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 data leakage by applying nested cross-validation during model training.
• B. Address data leakage by removing features highly correlated with the target value.
• C. Address the model overfitting by using a less complex algorithm.
• D. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

Answer: A Explanation:
https://towardsdatascience.com/time-series-nested-cross-validation-76adba623eb9
NEW QUESTION # 105
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 = Pub/Sub, 2 = Datastore
• B. 1 = Dataflow, 2 = BigQuery
• C. 1 = Cloud Function, 2 = Cloud SQL
• D. 1 = Dataflow, 2 = Cloud SQL

Answer: B Explanation:
https://github.com/GoogleCloudPlatform/dataflow-contact-center-speech-analysis
NEW QUESTION # 106
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