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Google Associate-Data-Practitioner Exam Syllabus Topics:

Topic	Details
Topic 1	Data Analysis and Presentation: This domain assesses the competencies of Data Analysts in identifying data trends, patterns, and insights using BigQuery and Jupyter notebooks. Candidates will define and execute SQL queries to generate reports and analyze data for business questions.\| Data Pipeline Orchestration: This section targets Data Analysts and focuses on designing and implementing simple data pipelines. Candidates will select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL.
Topic 2	Data Preparation and Ingestion: This section of the exam measures the skills of Google Cloud Engineers and covers the preparation and processing of data. Candidates will differentiate between various data manipulation methodologies such as ETL, ELT, and ETLT. They will choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery. A key skill measured is effectively assessing data quality before ingestion.
Topic 3	Data Management: This domain measures the skills of Google Database Administrators in configuring access control and governance. Candidates will establish principles of least privilege access using Identity and Access Management (IAM) and compare methods of access control for Cloud Storage. They will also configure lifecycle management rules to manage data retention effectively. A critical skill measured is ensuring proper access control to sensitive data within Google Cloud services

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Google Cloud Associate Data Practitioner Sample Questions (Q86-Q91):

NEW QUESTION # 86
You want to build a model to predict the likelihood of a customer clicking on an online advertisement. You have historical data in BigQuery that includes features such as user demographics, ad placement,and previous click behavior. After training the model, you want to generate predictions on new data. Which model type should you use in BigQuery ML?

A. Linear regression
B. Logistic regression
C. K-means clustering
D. Matrix factorization

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:
Predicting the likelihood of a click (binary outcome: click or no-click) requires a classification model.
BigQuery ML supports this use case with logistic regression.
* Option A: Linear regression predicts continuous values, not probabilities for binary outcomes.
* Option B: Matrix factorization is for recommendation systems, not binary prediction.
* Option C: Logistic regression predicts probabilities for binary classification (e.g., click likelihood), ideal for this scenario and supported in BigQuery ML.

NEW QUESTION # 87
You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

A. Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.
B. Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.
C. Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
D. Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline's metrics, such as CPU utilization and memory usage.

Answer: A

Explanation:
To troubleshoot intermittent failures in a Dataflow pipeline, you should use Cloud Logging to view detailed error messages in the pipeline's logs. These logs provide insights into the specific issues causing failures, such as data format errors or resource limitations. Additionally, you should use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization, memory usage, and throughput, to identify performance bottlenecks or resource constraints that may contribute to the failures. This approach provides a comprehensive view of the pipeline's health and helps pinpoint the root cause of the intermittent issues.

NEW QUESTION # 88
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rowsand transactional data. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost.
What should you do?

A. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
B. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
C. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
D. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.

Answer: C

Explanation:
Using Database Migration Service (DMS) to replicate the MySQL database to a Cloud SQL for MySQL instance is the best approach. DMS is a fully managed service designed for migrating databases to Google Cloud with minimal downtime and cost. It supports continuous data replication, ensuring data integrity during the migration process, and handles schema and data transfer efficiently. This solution is particularly suited for large tables and transactional data, as it maintains real-time synchronization between the source and target databases, minimizing downtime for the migration.

NEW QUESTION # 89
You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
B. Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
C. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
D. Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies.
Set up scheduled alerts based when anomalies or inconsistencies occur.

Answer: B

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Cloud Monitoring is the recommended service for monitoring Google Cloud services, including Dataflow.
It allows you to track key metrics like system lag, element throughput, and error rates.
Alerting policies in Cloud Monitoring can trigger notifications based on metric thresholds.
Why other options are incorrect:B: The Dataflow job monitoring interface is useful for visualization, but Cloud Monitoring provides more comprehensive alerting.
C: BigQuery is for analyzing the processed data, not monitoring the pipeline itself. Also Cloud Storage is not where the data resides during processing.
D: Cloud Logging is useful for viewing logs, but Cloud Monitoring is better for metric-based alerting.

NEW QUESTION # 90
You are designing an application that will interact with several BigQuery datasets. You need to grant the application's service account permissions that allow it to query and update tables within the datasets, and list all datasets in a project within your application. You want to follow the principle of least privilege. Which pre- defined IAM role(s) should you apply to the service account?

A. roles/bigquery.user and roles/bigquery.filteredDataViewer
B. roles/bigquery.jobUser and roles/bigquery.dataOwner
C. roles/bigquery.admin
D. roles/bigquery.connectionUser and roles/bigquery.dataViewer

Answer: B

Explanation:
* roles/bigquery.jobUser:
* This role allows a user or service account to run BigQuery jobs, including queries. This is necessary for the application to interact with and query the tables.
* From Google Cloud documentation: "BigQuery Job User can run BigQuery jobs, including queries, load jobs, export jobs, and copy jobs."
* roles/bigquery.dataOwner:
* This role grants full control over BigQuery datasets and tables. It allows the service account to update tables, which is a requirement of the application.
* From Google Cloud documentation: "BigQuery Data Owner can create, delete, and modify BigQuery datasets and tables. BigQuery Data Owner can also view data and run queries."
* Why other options are incorrect:
* B. roles/bigquery.connectionUser and roles/bigquery.dataViewer:
* roles/bigquery.connectionUser is used for external connections, which is not required for this task. roles/bigquery.dataViewer only allows viewing data, not updating it.
* C. roles/bigquery.admin:
* roles/bigquery.admin grants excessive permissions. Following the principle of least privilege, this role is too broad.
* D. roles/bigquery.user and roles/bigquery.filteredDataViewer:
* roles/bigquery.user grants the ability to run queries, but not the ability to modify data. roles
/bigquery.filteredDataViewer only provides permission to view filtered data, which is not sufficient for updating tables.
* Principle of Least Privilege:
* The principle of least privilege is a security concept that states that a user or service account should be granted only the permissions necessary to perform its intended tasks.
* By assigning roles/bigquery.jobUser and roles/bigquery.dataOwner, we provide the application with the exact permissions it needs without granting unnecessary access.
* Google Cloud Documentation References:
* BigQuery IAM roles:https://cloud.google.com/bigquery/docs/access-control-basic-roles
* IAM best practices:https://cloud.google.com/iam/docs/best-practices-for-using-iam

NEW QUESTION # 91
......

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100% Pass Perfect Google - Associate-Data-Practitioner Valid Exam Format

Google Associate-Data-Practitioner Exam Syllabus Topics:

Associate-Data-Practitioner Latest Exam Pass4sure, Associate-Data-Practitioner Latest Exam Guide

Google Cloud Associate Data Practitioner Sample Questions (Q86-Q91):

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