[PDE CERTIFICATE - EXAMTOPIC] DUMPS Q6-Q10

Google Professional Data Engineer Certificate EXAMTOPIC DUMPS Q6-Q10

Q 6.

Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

• ❌ A. Issue a command to restart the database servers.
• ⭕ B. Retry the query with exponential backoff, up to a cap of 15 minutes.
  → Google App engine create applications that use Cloud SQL database connections effectively. Exponential checking is best practice. By using exponential backoff prevents your application from sending an unresponsive number of connection requests when it can't connect to the database.
• ❌ C. Retry the query every second until it comes back online to minimize staleness of data.
• ❌ D. Reduce the query frequency to once every hour until the database comes back online.

Cloud SQL database connections

Managing database connections | Cloud SQL for MySQL | Google Cloud

• Exponential backoff
  If your application attempts to connect to the database and does not succeed, the database could be temporarily unavailable. In this case, sending too many simultaneous connection requests might waste additional database resources and increase the time needed to recover. Using exponential backoff prevents your application from sending an unresponsive number of connection requests when it can't connect to the database.
  • UNAVAILABLE, DEADLINE_EXCEEDED ⇒ Retry using exponential backoff. (best way to connect the database again in case of failure.)

Q 7.

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

• ⭕ A. Linear regression
  → REGRESSION vs classification : Target value that you are predicting is numeric/continuous
  → Linear regression can be done on ordinary CPU's.
• ❌ B. Logistic classification
• ❌ C. Recurrent neural network
  → require resource intensive machines(i.e GPU's).
• ❌ D. Feedforward neural network
  → require resource intensive machines(i.e GPU's).

Q 9.

Your company is using WILDCARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:

# Syntax error : Expected end of statement but got "-" at [4:11]

SELECT age -

FROM -
bigquery-public-data.noaa_gsod.gsod

WHERE -
age != 99
AND_TABLE_SUFFIX = "˜1929'

ORDER BY -
age DESC

Which table name will make the SQL statement work correctly?

• ❌ A. "˜bigquery-public-data.noaa_gsod.gsod"˜
  → Quotation marks do not work
• ❌ B. bigquery-public-data.noaa_gsod.gsod*
  → NOT valid because it isn't properly quoted with backticks
• ❌ C. '˜bigquery-public-data.noaa_gsod.gsod'*
  → Quotation marks do not work

⭕ D.

 `˜bigquery-public-data.noaa\_gsod.gsod*`

BigQuery - Wildcard

Wildcard tables | BigQuery | Google Cloud

Enclose table names with wildcards in backticks

#standardSQL
/* Valid standard SQL query */
SELECT
  max
FROM
  `bigquery-public-data.noaa_gsod.gsod*`
WHERE
  max != 9999.9 # code for missing data
  AND _TABLE_SUFFIX = '1929'
ORDER BY
  max DESC

The wildcard table name contains the special character (*), which means that you must enclose the wildcard table name in backtick (`) characters.

Q 10

Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

• A. Disable writes to certain tables.
• ⭕ B. Restrict access to tables by role.
• C. Ensure that the data is encrypted at all times.
• ⭕ D. Restrict BigQuery API access to approved users.
• E. Segregate data across multiple tables or databases.
• ⭕ F. Use Google Stackdriver Audit Logging to determine policy violations.

BigQuery permissions and predefined IAM roles

You can grant access at the following BigQuery resource levels:

1. organization or Google Cloud project level
2. dataset level
3. table or view level

Access control on table level in BigQuery

Access control on table level is now possible in BigQuery

Stackdriver audit logs

Stackdriver for store, search, analyze, monitor, and alert

• Audit Logs : to review data access (BigQuery)
• Stackdriver Monitoring : to see the usage of BigQuery query slots.
• Stackdriver Trace : a distributed tracing system for Google Cloud Platform that collects latency data from Google App Engine, Google HTTP(S) load balancers, and applications instrumented with the Stackdriver Trace SDKs, and displays it in near real time in the Google Cloud Platform Console on log data and events.