2025 TEST ASSOCIATE-DATA-PRACTITIONER DUMPS DEMO 100% PASS | THE BEST NEW GOOGLE CLOUD ASSOCIATE DATA PRACTITIONER PRACTICE MATERIALS PASS FOR SURE

2025 Test Associate-Data-Practitioner Dumps Demo 100% Pass | The Best New Google Cloud Associate Data Practitioner Practice Materials Pass for sure

2025 Test Associate-Data-Practitioner Dumps Demo 100% Pass | The Best New Google Cloud Associate Data Practitioner Practice Materials Pass for sure

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Google Associate-Data-Practitioner Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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

Google Cloud Associate Data Practitioner Sample Questions (Q76-Q81):

NEW QUESTION # 76
Your retail company collects customer data from various sources:
Online transactions: Stored in a MySQL database

Customer feedback: Stored as text files on a company server

Social media activity: Streamed in real-time from social media platforms

You are designing a data pipeline to extract this data. Which Google Cloud storage system(s) should you select for further analysis and ML model training?

  • A. 1. Online transactions: Bigtable
    2. Customer feedback: Cloud Storage
    3. Social media activity: CloudSQL for MySQL
  • B. 1. Online transactions: Cloud Storage
    2. Customer feedback: Cloud Storage
    3. Social media activity: Cloud Storage
  • C. 1. Online transactions: BigQuery
    2. Customer feedback: Cloud Storage
    3. Social media activity: BigQuery
  • D. 1. Online transactions: Cloud SQL for MySQL
    2. Customer feedback: BigQuery
    3. Social media activity: Cloud Storage

Answer: C

Explanation:
Online transactions:Storing the transactional data inBigQueryis ideal because BigQuery is a serverless data warehouse optimized for querying and analyzing structured data at scale. It supports SQL queries and is suitable for structured transactional data.
Customer feedback:Storing customer feedback inCloud Storageis appropriate as it allows you to store unstructured text files reliably and at a low cost. Cloud Storage also integrates well with data processing and ML tools for further analysis.
Social media activity:Storing real-time social media activity inBigQueryis optimal because BigQuery supports streaming inserts, enabling real-time ingestion and analysis of data. This allows immediate analysis and integration into dashboards or ML pipelines.


NEW QUESTION # 77
You have a Cloud SQL for PostgreSQL database that stores sensitive historical financial data. You need to ensure that the data is uncorrupted and recoverable in the event that the primary region is destroyed. The data is valuable, so you need to prioritize recovery point objective (RPO) over recovery time objective (RTO). You want to recommend a solution that minimizes latency for primary read and write operations. What should you do?

  • A. Configure the Cloud SQL for PostgreSQL instance for multi-region backup locations.
  • B. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with asynchronous replication to a secondary instance in a different region.
  • C. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with synchronous replication to a secondary instance in a different zone.
  • D. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA). Back up the Cloud SQL for PostgreSQL database hourly to a Cloud Storage bucket in a different region.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:
The priorities are data integrity, recoverability after a regional disaster, low RPO (minimal data loss), and low latency for primary operations. Let's analyze:
* Option A: Multi-region backups store point-in-time snapshots in a separate region. With automated backups and transaction logs, RPO can be near-zero (e.g., minutes), and recovery is possible post- disaster. Primary operations remain in one zone, minimizing latency.
* Option B: Regional HA (failover to another zone) with hourly cross-region backups protects against zone failures, but hourly backups yield an RPO of up to 1 hour-too high for valuable data. Manual backup management adds overhead.
* Option C: Synchronous replication to another zone ensures zero RPO within a region but doesn't protect against regional loss. Latency increases slightly due to sync writes across zones.


NEW QUESTION # 78
Your team uses Google Sheets to track budget data that is updated daily. The team wants to compare budget data against actual cost data, which is stored in a BigQuery table. You need to create a solution that calculates the difference between each day's budget and actual costs. You want to ensure that your team has access to daily-updated results in Google Sheets. What should you do?

  • A. Download the budget data as a CSV file, and upload the CSV file to create a new BigQuery table. Join the actual cost table with the new BigQuery table, and save the results as a CSV file. Open the CSV file in Google Sheets.
  • B. Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table, and open it by using Connected Sheets.
  • C. Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table as a CSV file and open the file in Google Sheets.
  • D. Download the budget data as a CSV file and upload the CSV file to a Cloud Storage bucket. Create a new BigQuery table from Cloud Storage, and join the actual cost table with it. Open the joined BigQuery table by using Connected Sheets.

Answer: B

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why D is correct:Creating a BigQuery external table directly from the Google Sheet allows for real-time updates.
Joining the external table with the actual cost table in BigQuery performs the calculation.
Connected Sheets allows the team to access and analyze the results directly in Google Sheets, with the data being updated.
Why other options are incorrect:A: Saving as a CSV file loses the live connection and daily updates.
B: Downloading and uploading as a CSV file adds unnecessary steps and loses the live connection.
C: Same issue as B, losing the live connection.


NEW QUESTION # 79
Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys. What should you do?

  • A. Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.
  • B. Use customer-managed encryption keys (CMEK).
  • C. Use Google-managed encryption keys (GMEK).
  • D. Use customer-supplied encryption keys (CSEK).

Answer: C

Explanation:
Using Google-managed encryption keys (GMEK) is the best choice when you want to encrypt sensitive data in Cloud Storage without the operational overhead of managing encryption keys. GMEK is the default encryption mechanism in Google Cloud, and it ensures that data is automatically encrypted at rest with no additional setup or maintenance required. It provides strong security while eliminating the need for manual key management.
Google Cloud encrypts all data at rest by default, and the simplest way to avoid key management overhead is to use Google-managed encryption keys (GMEK).
* Option A: GMEK is fully managed by Google, requiring no user intervention, and meets the requirement of no operational overhead while ensuring encryption.
* Option B: CMEK requires managing keys in Cloud KMS, adding operational overhead.
* Option C: CSEK requires users to supply and manage keys externally, increasing complexity significantly.


NEW QUESTION # 80
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

  • A. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
  • B. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
  • C. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
  • D. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.

Answer: A

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:AutoML Natural Language is designed for text classification tasks, including sentiment analysis, and can handle diverse language patterns without extensive preprocessing.
AutoML can train a custom model with minimal coding.
Why other options are incorrect:A: Unnecessary extra preprocessing. AutoML can handle the raw data.
C: Dataproc and Spark are overkill for this task. AutoML is more efficient and easier to use.
D: Developing a custom TensorFlow model requires significant expertise and time, which is not efficient for this scenario.


NEW QUESTION # 81
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