TRUSTED ASSOCIATE-DATA-PRACTITIONER EXAM RESOURCE - NEW ASSOCIATE-DATA-PRACTITIONER TEST PATTERN

Trusted Associate-Data-Practitioner Exam Resource - New Associate-Data-Practitioner Test Pattern

Trusted Associate-Data-Practitioner Exam Resource - New Associate-Data-Practitioner Test Pattern

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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

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Google Cloud Associate Data Practitioner Sample Questions (Q42-Q47):

NEW QUESTION # 42
You work for a retail company that 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 platformsYou need to design a data pipeline to extract and load the data into the appropriate Google Cloud storage system(s) for further analysis and ML model training. What should you do?

  • A. Extract and load the online transactions data into Bigtable. Import the customer feedback data into Cloud Storage. Store the social media activity in Cloud SQL for MySQL.
  • B. Extract and load the online transactions data into BigQuery. Load the customer feedback data into Cloud Storage. Stream the social media activity by using Pub/Sub and Dataflow, and store the data in BigQuery.
  • C. Extract and load the online transactions data, customer feedback data, and social media activity into Cloud Storage.
  • D. Copy the online transactions data into Cloud SQL for MySQL. Import the customer feedback into BigQuery. Stream the social media activity into Cloud Storage.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:
The pipeline must extract diverse data types and load them into systems optimized for analysis and ML. Let's assess:
* Option A: Cloud SQL for transactions keeps data relational but isn't ideal for analysis/ML (less scalable than BigQuery). BigQuery for feedback is fine but skips staging. Cloud Storage for streaming social media loses real-time context and requires extra steps for analysis.
* Option B: BigQuery for transactions (via export from MySQL) supports analysis/ML with SQL. Cloud Storage stages feedback text files for preprocessing, then BigQuery ingestion. Pub/Sub and Dataflow stream social media into BigQuery, enabling real-time analysis-optimal for all sources.
* Option C: Cloud Storage for all data is a staging step, not a final solution for analysis/ML, requiring additional pipelines.


NEW QUESTION # 43
You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

  • A. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
  • B. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
  • C. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
  • D. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:
For a small dataset with simple transformations (filtering, aggregation), Google recommends leveraging BigQuery's native SQL capabilities to minimize cost and complexity.
* Option A: Dataproc with Spark is overkill for a small dataset, incurring cluster management costs and setup time.
* Option B: BigQuery can load data directly from Cloud Storage (e.g., CSV, JSON) and perform transformations using SQL in a serverless manner, avoiding additional service costs. This is the most efficient and cost-effective approach.
* Option C: Cloud Data Fusion is suited for complex ETL but adds overhead (instance setup, UI design) unnecessary for simple tasks.


NEW QUESTION # 44
You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

  • A. Use the gcloud storage command to transfer the data over the internet.
  • B. Use Cloud Client Libraries to transfer the data over the internet.
  • C. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to Cloud Storage.
  • D. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation:
Transferring 300 TB over a 100 Mbps connection would take an impractical amount of time (over 300 days at theoretical maximum speed, ignoring real-world constraints like latency). Google Cloud provides the Transfer Appliance for large-scale, time-sensitive transfers.
* Option A: Cloud Client Libraries over the internet would be slow and unreliable for 300 TB due to bandwidth limitations.
* Option B: The gcloud storage command is similarly constrained by internet speed and not designed for such large transfers.
* Option C: Compressing and splitting across multiple providers adds complexity and isn't a Google- supported method for Cloud Storage ingestion.


NEW QUESTION # 45
You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

  • A. Evaluate the data skewness.
  • B. Evaluate data drift.
  • C. Compare the confusion matrix.
  • D. Compare the two different models.

Answer: B

Explanation:
Evaluating data drift involves analyzing changes in the distribution of the current serving data compared to the historical data used to train the model. If significant drift is detected, it indicates that the data patterns have changed over time, which can impact the model's performance. This analysis helps determine whether retraining the model is necessary to ensure its predictions remain accurate and relevant. Data drift evaluation is a standard approach for monitoring machine learning models over time.


NEW QUESTION # 46
Your organization consists of two hundred employees on five different teams. The leadership team is concerned that any employee can move or delete all Looker dashboards saved in the Shared folder. You need to create an easy-to-manage solution that allows the five different teams in your organization to view content in the Shared folder, but only be able to move or delete their team-specific dashboard. What should you do?

  • A. 1. Move all team-specific content into the dashboard owner s personal folder. 2. Change the access level of the Shared folder to View for the All Users group. 3. Instruct each user to create content for their team in the user's personal folder.
  • B. 1. Create Looker groups representing each of the five different teams, and add users to their corresponding group. 2. Create five subfolders inside the Shared folder. Grant each group the View access level to their corresponding subfolder.
  • C. 1. Change the access level of the Shared folder to View for the All Users group. 2. Create Looker groups representing each of the five different teams, and add users to their corresponding group. 3.
    Create five subfolders inside the Shared folder. Grant each group the Manage Access, Edit access level to their corresponding subfolder.
  • D. 1. Change the access level of the Shared folder to View for the All Users group. 2. Create five subfolders inside the Shared folder. Grant each team member the Manage Access, Edit access level to their corresponding subfolder.

Answer: C

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Setting the Shared folder to "View" ensures everyone can see the content.
Creating Looker groups simplifies access management.
Subfolders allow granular permissions for each team.
Granting "Manage Access, Edit" allows teams to modify only their own content.
Why other options are incorrect:A: Grants View access only, so teams can't edit.
B: Moving content to personal folders defeats the purpose of sharing.
D: Grants edit access to all members of the team, not the team as a whole, which is not ideal.


NEW QUESTION # 47
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